library(dplyr)
library(ggplot2)
library(pheatmap)
library(RColorBrewer)
library(reshape2)
library(ggpubr)
library(ComplexHeatmap)
library(circlize)
library(readxl)
library(plyr)
library(knitr)
library(stargazer)
setwd("~/Dropbox (Sydney Uni)/YunweiZhang/WeeklyProgress/R code/benchmarking/202104summaryresult")plot_fun=function(cox1, cox2, cox3, cox4, pcox1, pcox2, pcox3, rsf1, rsf2, mtlr1, dnnsurv1, coxboost,gacox,gamtlr,gacoxboost,limmamtlr,limmacoxboost,survivalsvm,deepsurv,deephit){
cox1dt=bind_rows(cox1, .id = "column_label")
cox1value=colMeans(cox1dt[,-1],na.rm = TRUE)
cox2dt=bind_rows(cox2, .id = "column_label")
cox2value=colMeans(cox2dt[,-1],na.rm = TRUE)
cox3dt=bind_rows(cox3, .id = "column_label")
cox3value=colMeans(cox3dt[,-1],na.rm = TRUE)
#this contains NA
cox4=cox4[lapply(cox4,length)>1]
cox4dt=bind_rows(cox4, .id = "column_label")
cox4value=colMeans(cox4dt[,-1],na.rm = TRUE)
#cox5dt=bind_rows(cox5, .id = "column_label")
#cox5value=colMeans(cox5dt[,-1],na.rm = TRUE)
#cox6dt=bind_rows(cox6, .id = "column_label")
#cox6value=colMeans(cox6dt[,-1],na.rm = TRUE)
pcox1dt=bind_rows(pcox1, .id = "column_label")
pcox1value=colMeans(pcox1dt[,-1],na.rm = TRUE)
pcox2dt=bind_rows(pcox2, .id = "column_label")
pcox2value=colMeans(pcox2dt[,-1],na.rm = TRUE)
pcox3dt=bind_rows(pcox3, .id = "column_label")
pcox3value=colMeans(pcox3dt[,-1],na.rm = TRUE)
rsf1dt=bind_rows(rsf1, .id = "column_label")
rsf1value=colMeans(rsf1dt[,-1],na.rm = TRUE)
rsf2dt=bind_rows(rsf2, .id = "column_label")
rsf2value=colMeans(rsf2dt[,-1],na.rm = TRUE)
mtlr1dt=bind_rows(mtlr1, .id = "column_label")
mtlr1value=colMeans(mtlr1dt[,-1],na.rm = TRUE)
dnnsurv1dt=bind_rows(dnnsurv1, .id = "column_label")
dnnsurv1value=colMeans(dnnsurv1dt[,-1],na.rm = TRUE)
coxboostdt=bind_rows(coxboost, .id = "column_label")
coxboostvalue=colMeans(coxboostdt[,-1],na.rm = TRUE)
gacoxdt=bind_rows(gacox, .id = "column_label")
gacoxvalue=colMeans(gacoxdt[,-1],na.rm = TRUE)
gamtlrdt=bind_rows(gamtlr, .id = "column_label")
gamtlrvalue=colMeans(gamtlrdt[,-1],na.rm = TRUE)
gacoxboostdt=bind_rows(gacoxboost, .id = "column_label")
gacoxboostvalue=colMeans(gacoxboostdt[,-1],na.rm = TRUE)
limmamtlrdt=bind_rows(limmamtlr, .id = "column_label")
limmamtlrvalue=colMeans(limmamtlrdt[,-1],na.rm = TRUE)
limmacoxboostdt=bind_rows(limmacoxboost, .id = "column_label")
limmacoxboostvalue=colMeans(limmacoxboostdt[,-1],na.rm = TRUE)
survivalsvmdt=bind_rows(survivalsvm, .id = "column_label")
survivalsvmvalue=colMeans(survivalsvmdt[,-1],na.rm = TRUE)
deepsurvdt=bind_rows(deepsurv, .id = "column_label")
deepsurvvalue=colMeans(deepsurvdt[,-1],na.rm = TRUE)
deephitdt=bind_rows(deephit, .id = "column_label")
deephitvalue=colMeans(deephitdt[,-1],na.rm = TRUE)
nb.cols <- 20
mycolors <- colorRampPalette(brewer.pal(8, "Set2"))(nb.cols)
plotdt1=rbind.data.frame(cox1value,cox2value,cox3value,cox4value,pcox1value,pcox2value,pcox3value,rsf1value,rsf2value,mtlr1value,dnnsurv1value,coxboostvalue,gacoxvalue,gamtlrvalue,gacoxboostvalue,limmamtlrvalue,limmacoxboostvalue,survivalsvmvalue,deepsurvvalue,deephitvalue)
rownames(plotdt1)=c("cox1","cox2","cox3","cox4","pcox1","pcox2","pcox3","rsf1","rsf2","mtlr1","dnnsurv1","coxboost","gacox","gamtlr","gacoxboost","limmamtlr","limmacoxboost","survivalsvm","deepsurv","deephit")
colnames(plotdt1)=names(pcox1value)
head(plotdt1)
plotdt2=t(plotdt1)
#head(plotdt2)
#plotdt3=apply(plotdt2, MARGIN = 2, FUN = function(X) (X - min(X))/diff(range(X)))
plotdt3=plotdt2[-c(2,4,5:10),]
#plotdt3[plotdt3>1]=NA #out of range (0,1) is definde as NA
breaksList = seq(0.5, 1, by = 0.1)
#pheatmap(as.matrix(plotdt3),cellwidth = 15, cellheight = 15, fontsize = 8,cluster_rows = FALSE,cluster_cols = FALSE,color =colorRampPalette(c("#999999", "#E69F00", "#56B4E9"))(length(breaksList)),breaks = breaksList)
p1=pheatmap(as.matrix(plotdt3),cellwidth = 15, cellheight = 15, fontsize = 8,cluster_rows = FALSE,cluster_cols = FALSE,color =colorRampPalette(rev(brewer.pal(n = 7, name = "Pastel1")))(length(breaksList)),breaks = breaksList)
#print(p1)
# create data frame with all values
colnames(cox1dt)=colnames(cox2dt)=colnames(cox3dt)=colnames(cox4dt)=colnames(pcox1dt)=colnames(pcox2dt)=colnames(pcox3dt)=colnames(rsf1dt)=colnames(rsf2dt)=colnames(mtlr1dt)=colnames(dnnsurv1dt)=colnames(coxboostdt)=colnames(gacoxdt)=colnames(gamtlrdt)=colnames(gacoxboostdt)=colnames(limmamtlrdt)=colnames(limmacoxboostdt)=colnames(survivalsvmdt)=colnames(deepsurvdt)=colnames(deephitdt)=c("model","hc","bc","unoc","ghc","bs1","bs2","bs3","bs4","bs5","bs6","auc1","auc2","auc3","auc4","auc5","auc6","auc7","auc8","auc9","auc10","auc11","auc12","auc13","auc14","auc15","auc")
widedt=do.call("rbind", list(cox1dt,cox2dt,cox3dt,cox4dt,pcox1dt,pcox2dt,pcox3dt,rsf1dt,rsf2dt,mtlr1dt,dnnsurv1dt,coxboostdt,gacoxdt,gamtlrdt,gacoxboostdt,limmamtlrdt,limmacoxboostdt,survivalsvmdt,deepsurvdt,deephitdt))
widedt[,1]=c(rep("cox1",nrow(cox1dt)),rep("cox2",nrow(cox2dt)),rep("cox3",nrow(cox3dt)),rep("cox4",nrow(cox4dt)),rep("pcox1",nrow(pcox1dt)),rep("pcox2",nrow(pcox2dt)),rep("pcox3",nrow(pcox3dt)),rep("rsf1",nrow(rsf1dt)),rep("rsf2",nrow(rsf2dt)),rep("mtlr1",nrow(mtlr1dt)),rep("dnnsurv1",nrow(dnnsurv1dt)),rep("coxboost",nrow(coxboostdt)),rep("gacox",nrow(gacoxdt)),rep("gamtlr",nrow(gamtlrdt)),rep("gacoxboost",nrow(gacoxboostdt)),rep("limmamtlr",nrow(limmamtlrdt)),rep("limmacoxboost",nrow(limmacoxboostdt)),rep("survivalsvm",nrow(survivalsvmdt)),rep("deepsurv",nrow(deepsurvdt)),rep("deephit",nrow(deephitdt)))
# grouped boxplot
longdt=melt(widedt,id.vars = "model",na.rm = TRUE)
#dim(longdt)
#any(is.na(longdt))
# get the hc
plotdt=longdt[longdt$variable=="hc",]
#plotdt[plotdt$value <0.5 | plotdt$value >0.99,3] <- NA
#plotdt[plotdt$value < quantile(plotdt$value, 0.85,na.rm = T) | plotdt$value > quantile(plotdt$value, 0.15,na.rm = T), ]
plotdt$model=factor(plotdt$model,levels=c("cox1","cox2","cox3","cox4","pcox1","pcox2","pcox3","rsf1","rsf2","mtlr1","dnnsurv1","coxboost","gacox","gamtlr","gacoxboost","limmamtlr","limmacoxboost","survivalsvm","deepsurv","deephit"))
g1=ggplot(plotdt, aes(x=model, y=value, fill=model)) + geom_boxplot(lwd=0.5)+scale_fill_manual(values = mycolors,drop=FALSE)+theme_bw() #+scale_y_continuous(limits=c(0.5,1))
# get all the cindex
plotdt=longdt[longdt$variable=="hc"|longdt$variable=="bc"|longdt$variable=="unoc"|longdt$variable=="ghc",]
plotdt$model=factor(plotdt$model,levels=c("cox1","cox2","cox3","cox4","pcox1","pcox2","pcox3","rsf1","rsf2","mtlr1","dnnsurv1","coxboost","gacox","gamtlr","gacoxboost","limmamtlr","limmacoxboost","survivalsvm","deepsurv","deephit"))
g2=ggplot(plotdt, aes(x=model, y=value, fill=variable)) + geom_boxplot(lwd=0.5)+scale_fill_manual(values = mycolors)+theme_bw() #+scale_y_continuous(limits=c(0.5,1))
# get all the auc
plotdt=longdt[longdt$variable=="auc1"|longdt$variable=="auc2"|longdt$variable=="auc3"|longdt$variable=="auc4",]
plotdt$model=factor(plotdt$model,levels=c("cox1","cox2","cox3","cox4","pcox1","pcox2","pcox3","rsf1","rsf2","mtlr1","dnnsurv1","coxboost","gacox","gamtlr","gacoxboost","limmamtlr","limmacoxboost","survivalsvm","deepsurv","deephit"))
g3=ggplot(plotdt, aes(x=model, y=value, fill=variable)) + geom_boxplot(lwd=0.5)+scale_fill_manual(values = mycolors)+theme_bw() #+scale_y_continuous(limits=c(0.5,1))
# get all the bs
plotdt=longdt[longdt$variable=="bs1"|longdt$variable=="bs2",]
plotdt$model=factor(plotdt$model,levels=c("cox1","cox2","cox3","cox4","pcox1","pcox2","pcox3","rsf1","rsf2","mtlr1","dnnsurv1","coxboost","gacox","gamtlr","gacoxboost","limmamtlr","limmacoxboost","survivalsvm","deepsurv","deephit"))
g4=ggplot(plotdt, aes(x=model, y=value, fill=variable)) + geom_boxplot(lwd=0.1,outlier.shape = NA)+scale_fill_manual(values = mycolors) +theme_bw()
# get bs1
plotdt=longdt[longdt$variable=="bs1",]
plotdt$model=factor(plotdt$model,levels=c("cox1","cox2","cox3","cox4","pcox1","pcox2","pcox3","rsf1","rsf2","mtlr1","dnnsurv1","coxboost","gacox","gamtlr","gacoxboost","limmamtlr","limmacoxboost","survivalsvm","deepsurv","deephit"))
g5=ggplot(plotdt, aes(x=model, y=value, fill=model)) + geom_boxplot(lwd=0.5,outlier.shape = NA)+scale_fill_manual(values = mycolors, drop=FALSE)+theme_bw()
# get auc curve
plotdt=longdt[longdt$variable=="auc1"|longdt$variable=="auc2"|longdt$variable=="auc3"|longdt$variable=="auc4"|longdt$variable=="auc5"|longdt$variable=="auc6"|longdt$variable=="auc7"|longdt$variable=="auc8"|longdt$variable=="auc9"|longdt$variable=="auc10"|longdt$variable=="auc11"|longdt$variable=="auc12"|longdt$variable=="auc13"|longdt$variable=="auc14"|longdt$variable=="auc15",]
#plotdt[plotdt$value >1 | plotdt$value==0,3] <- NA
plotdt2 <- plotdt %>%dplyr::group_by(variable,model) %>%dplyr::summarize(Mean = mean(value))
plotdt2$timepoint=as.numeric(gsub(".*?([0-9]+).*", "\\1", plotdt2$variable))
# g6=ggplot(data = plotdt2, aes(timepoint, Mean)) +
# geom_line() +geom_smooth()+
# labs(title = " time-dependent AUC",
# y = "value", x = "time points") +
# facet_wrap(~ model)#+scale_y_continuous(limits=c(0,1))
plotdt2$model=factor(plotdt2$model,levels=c("cox1","cox2","cox3","cox4","pcox1","pcox2","pcox3","rsf1","rsf2","mtlr1","dnnsurv1","coxboost","gacox","gamtlr","gacoxboost","limmamtlr","limmacoxboost","survivalsvm","deepsurv","deephit"))
names(mycolors)=levels(plotdt2$model)
g6=ggplot(plotdt2,aes(x=timepoint,y=Mean,color=model))+geom_line(aes(color=model))+scale_color_manual(values = mycolors, drop=FALSE)+theme_bw()
# print(g1)
# print(g2)
# print(g3)
# print(g4)
# print(g5)
# print(g6)
#print(ggarrange(g1,g2, g3,g4,g5,labels = c("c","c", "auc", "br","ibr"),ncol = 2, nrow = 3))
return(list(longdt,g1,g2,g3,g4,g5,g6,p1))
}characteristic_table=function(cox4){
cox4dt=bind_rows(cox4, .id = "column_label")
colnames(cox4dt)=c("model","hc","bc","unoc","ghc","bs1","bs2","bs3","bs4","bs5","bs6","auc1","auc2","auc3","auc4","auc5","auc6","auc7","auc8","auc9","auc10","auc11","auc12","auc13","auc14","auc15","auc")
proportion=NULL
summaryy=NULL
if(sum(rowSums(is.na(cox4dt))==26)!=0){
proportion=1-sum(rowSums(is.na(cox4dt))==26)/100
cox4dt=cox4dt[-which(rowSums(is.na(cox4dt))==26),]
summaryy=summary(cox4dt)}else{
proportion=1
summaryy=summary(cox4dt)
}
return(list(proportion,summaryy))
}empty_list=list()
for(i in 1:50){
empty_list[[i]]=data.frame(matrix(NA, ncol = 26, nrow = 5))
colnames(empty_list[[i]])=c("hc_acc5", "bc_acc5", "unoc_acc5" ,"ghc_acc5" , "bs1" , "bs2" ,"bs3","bs4", "bs5", "bs6" , "auc1" , "auc2","auc3" , "auc4","auc5" , "auc6", "auc7", "auc8", "auc9", "auc10" , "auc11" , "auc12" , "auc13", "auc14" , "auc15" , "auc")
}
cox1=readRDS("savedresults/pbc_cox1.rds")
cox2=readRDS("savedresults/pbc_bw_cox1.rds")
cox3=readRDS("savedresults/pbc_bw_cox2.rds")
cox4=readRDS("savedresults/pbc_bw_cox3.rds")
pcox1=readRDS("savedresults/pbc_p_cox1.rds")
pcox2=readRDS("savedresults/pbc_p_cox2.rds")
pcox3=readRDS("savedresults/pbc_p_cox3.rds")
rsf1=readRDS("savedresults/pbc_rsf1.rds")
rsf2=readRDS("savedresults/pbc_rsf2.rds")
mtlr1=readRDS("savedresults/pbc_mtlr.rds")
dnnsurv1=readRDS("savedresults/pbc_dnnsurv.rds")
coxboost=readRDS("savedresults/pbc_coxboost.rds")
gacox=readRDS("savedresults/pbc_ga_cox1.rds")
gamtlr=readRDS("savedresults/pbc_ga_mtlr.rds")
gacoxboost=readRDS("savedresults/pbc_ga_coxboost.rds")
limmamtlr=readRDS("savedresults/pbc_limma_mtlr.rds")
limmacoxboost=readRDS("savedresults/pbc_limma_coxboost.rds")
survivalsvm=readRDS("savedresults/pbc_survivalsvm.rds")
deepsurv=readRDS("savedresults/pbc_deepsurv.rds")
deephit=readRDS("savedresults/pbc_deephit.rds")
want=plot_fun(cox1, cox2, cox3, cox4, pcox1, pcox2, pcox3, rsf1, rsf2, mtlr1, dnnsurv1,coxboost,gacox,gamtlr,gacoxboost,limmamtlr,limmacoxboost,survivalsvm,deepsurv,deephit)pbcfull=want[[1]]
g1=want[[2]]
g2=want[[3]]
g3=want[[4]]
g4=want[[5]]
g5=want[[6]]
g6=want[[7]]
g7=want[[8]]
characteristic_table(dnnsurv1)[[1]]## [1] 0.8
cox1=readRDS("savedresults/veteran_cox1.rds")
cox2=readRDS("savedresults/veteran_bw_cox1.rds")
cox3=readRDS("savedresults/veteran_bw_cox2.rds")
cox4=readRDS("savedresults/veteran_bw_cox3.rds")
pcox1=readRDS("savedresults/veteran_p_cox1.rds")
pcox2=readRDS("savedresults/veteran_p_cox2.rds")
pcox3=readRDS("savedresults/veteran_p_cox3.rds")
rsf1=readRDS("savedresults/veteran_rsf1.rds")
rsf2=readRDS("savedresults/veteran_rsf2.rds")
mtlr1=readRDS("savedresults/veteran_mtlr.rds")
dnnsurv1=readRDS("savedresults/veteran_dnnsurv.rds")
coxboost=readRDS("savedresults/veteran_coxboost.rds")
gacox=readRDS("savedresults/veteran_ga_cox1.rds")
summ=0
for (i in 1:length(gacox)){
if (class(gacox[[i]])=="try-error"){
summ=summ+1
gacox[[i]]=data.frame(matrix(NA, ncol = 26, nrow = 5))}else{gacox[[i]]=gacox[[i]]}
colnames(gacox[[i]])=c("hc_acc5", "bc_acc5", "unoc_acc5" ,"ghc_acc5" , "bs1" , "bs2" ,"bs3","bs4", "bs5", "bs6" , "auc1" , "auc2","auc3" , "auc4","auc5" , "auc6", "auc7", "auc8", "auc9", "auc10" , "auc11" , "auc12" , "auc13", "auc14" , "auc15" , "auc")}
print(summ)## [1] 3
gamtlr=readRDS("savedresults/veteran_ga_mtlr.rds")
gacoxboost=readRDS("savedresults/veteran_ga_coxboost.rds")
limmamtlr=readRDS("savedresults/veteran_limma_mtlr.rds")
limmacoxboost=readRDS("savedresults/veteran_limma_coxboost.rds")
survivalsvm=readRDS("savedresults/veteran_survivalsvm.rds")
deepsurv=readRDS("savedresults/veteran_deepsurv.rds")
deephit=readRDS("savedresults/veteran_deephit.rds")
want=plot_fun(cox1, cox2, cox3, cox4, pcox1, pcox2, pcox3, rsf1, rsf2, mtlr1, dnnsurv1,coxboost,gacox,gamtlr,gacoxboost,limmamtlr,limmacoxboost,survivalsvm,deepsurv,deephit)veteranfull=want[[1]]
g8=want[[2]]
g9=want[[3]]
g10=want[[4]]
g11=want[[5]]
g12=want[[6]]
g13=want[[7]]
g14=want[[8]]
characteristic_table(dnnsurv1)[[1]]## [1] 0.66
## [1] 0.85
empty_list=list()
for(i in 1:50){
empty_list[[i]]=data.frame(matrix(NA, ncol = 26, nrow = 5))
colnames(empty_list[[i]])=c("hc_acc5", "bc_acc5", "unoc_acc5" ,"ghc_acc5" , "bs1" , "bs2" ,"bs3","bs4", "bs5", "bs6" , "auc1" , "auc2","auc3" , "auc4","auc5" , "auc6", "auc7", "auc8", "auc9", "auc10" , "auc11" , "auc12" , "auc13", "auc14" , "auc15" , "auc")
}
cox1=readRDS("savedresults/lung_cox1.rds")
cox2=readRDS("savedresults/lung_bw_cox1.rds")
cox3=readRDS("savedresults/lung_bw_cox2.rds")
cox4=readRDS("savedresults/lung_bw_cox3.rds")
pcox1=readRDS("savedresults/lung_p_cox1.rds")
pcox2=readRDS("savedresults/lung_p_cox2.rds")
pcox3=readRDS("savedresults/lung_p_cox3.rds")
rsf1=readRDS("savedresults/lung_rsf1.rds")
rsf2=readRDS("savedresults/lung_rsf2.rds")
mtlr1=readRDS("savedresults/lung_mtlr.rds")
dnnsurv1=readRDS("savedresults/lung_dnnsurv.rds")
coxboost=readRDS("savedresults/lung_coxboost.rds")
gacox=readRDS("savedresults/lung_ga_cox1.rds")
gamtlr=readRDS("savedresults/lung_ga_mtlr.rds")
gacoxboost=readRDS("savedresults/lung_ga_coxboost.rds")
limmamtlr=readRDS("savedresults/lung_limma_mtlr.rds")
limmacoxboost=readRDS("savedresults/lung_limma_coxboost.rds")
survivalsvm=readRDS("savedresults/lung_survivalsvm.rds")
deepsurv=readRDS("savedresults/lung_deepsurv.rds")
deephit=readRDS("savedresults/lung_deephit.rds")
want=plot_fun(cox1, cox2, cox3, cox4, pcox1, pcox2, pcox3, rsf1, rsf2, mtlr1, dnnsurv1,coxboost,gacox,gamtlr,gacoxboost,limmamtlr,limmacoxboost,survivalsvm,deepsurv,deephit)lungfull=want[[1]]
g15=want[[2]]
g16=want[[3]]
g17=want[[4]]
g18=want[[5]]
g19=want[[6]]
g20=want[[7]]
g21=want[[8]]
#ggsave(plot=g20,file="lung_auc.pdf",device = "pdf")
characteristic_table(dnnsurv1)[[1]]## [1] 1
empty_list=list()
for(i in 1:50){
empty_list[[i]]=data.frame(matrix(NA, ncol = 26, nrow = 5))
colnames(empty_list[[i]])=c("hc_acc5", "bc_acc5", "unoc_acc5" ,"ghc_acc5" , "bs1" , "bs2" ,"bs3","bs4", "bs5", "bs6" , "auc1" , "auc2","auc3" , "auc4","auc5" , "auc6", "auc7", "auc8", "auc9", "auc10" , "auc11" , "auc12" , "auc13", "auc14" , "auc15" , "auc")
}
cox1=readRDS("savedresults/anz_cox1.rds")
cox2=readRDS("savedresults/anz_bw_cox1.rds")
cox3=readRDS("savedresults/anz_bw_cox2.rds")
cox4=readRDS("savedresults/anz_bw_cox3.rds")
pcox1=readRDS("savedresults/anz_p_cox1.rds")
pcox2=readRDS("savedresults/anz_p_cox2.rds")
pcox3=readRDS("savedresults/anz_p_cox3.rds")
rsf1=readRDS("savedresults/anz_rsf1.rds")
rsf2=readRDS("savedresults/anz_rsf2.rds")
mtlr1=readRDS("savedresults/anz_mtlr.rds")
dnnsurv1=readRDS("savedresults/anz_dnnsurv.rds")
coxboost=readRDS("savedresults/anz_coxboost.rds")
gacox=readRDS("savedresults/anz_ga_cox1.rds")
gamtlr=readRDS("savedresults/anz_ga_mtlr.rds")
gacoxboost=readRDS("savedresults/anz_ga_coxboost.rds")
limmamtlr=readRDS("savedresults/anz_limma_mtlr.rds")
limmacoxboost=readRDS("savedresults/anz_limma_coxboost.rds")
survivalsvm=empty_list
deepsurv=readRDS("savedresults/anz_deepsurv.rds")
deephit=readRDS("savedresults/anz_deephit.rds")
want=plot_fun(cox1, cox2, cox3, cox4, pcox1, pcox2, pcox3, rsf1, rsf2, mtlr1, dnnsurv1,coxboost,gacox,gamtlr,gacoxboost,limmamtlr,limmacoxboost,survivalsvm,deepsurv,deephit)anzfull=want[[1]]
g22=want[[2]]
g23=want[[3]]
g24=want[[4]]
g25=want[[5]]
g26=want[[6]]
g27=want[[7]]
g28=want[[8]]
characteristic_table(dnnsurv1)[[1]]## [1] 1
empty_list=list()
for(i in 1:50){
empty_list[[i]]=data.frame(matrix(NA, ncol = 26, nrow = 5))
colnames(empty_list[[i]])=c("hc_acc5", "bc_acc5", "unoc_acc5" ,"ghc_acc5" , "bs1" , "bs2" ,"bs3","bs4", "bs5", "bs6" , "auc1" , "auc2","auc3" , "auc4","auc5" , "auc6", "auc7", "auc8", "auc9", "auc10" , "auc11" , "auc12" , "auc13", "auc14" , "auc15" , "auc")
}
cox1=readRDS("savedresults/us_cox1.rds")
cox2=empty_list
cox3=empty_list
cox4=empty_list
#pcox1=readRDS("US/cox4.rds")
# for (i in 1:length(dnnsurv1)){
# if (class(dnnsurv1[[i]])=="try-error")
# dnnsurv1[[i]]=data.frame(matrix(NA, ncol = 26, nrow = 5))
# colnames(dnnsurv1[[i]])=c("hc", "bc", "unoc" ,"ghc" , "br1" , "br2" ,"br3","br4", "br5", "br6" , "a1" , "a2","a3" , "a4","a5" , "a6", "a7", "a8", "a9", "a10" , "a11" , "a12" , "a13", "a14" , "a15" , "a")}
pcox1=readRDS("savedresults/us_p_cox1.rds")
pcox2=readRDS("savedresults/us_p_cox2.rds")
pcox3=readRDS("savedresults/us_p_cox3.rds")
rsf1=readRDS("savedresults/us_rsf1.rds")
rsf2=readRDS("savedresults/us_rsf2.rds")
mtlr1=readRDS("savedresults/us_mtlr.rds")
dnnsurv1=empty_list
coxboost=readRDS("savedresults/us_coxboost.rds")
gacox=empty_list
gamtlr=empty_list
gacoxboost=empty_list
limmamtlr=readRDS("savedresults/us_limma_mtlr.rds")
limmacoxboost=readRDS("savedresults/us_limma_coxboost.rds")
survivalsvm=empty_list
deepsurv=readRDS("savedresults/us_deepsurv.rds")
deephit=readRDS("savedresults/us_deephit.rds")
want=plot_fun(cox1, cox2, cox3, cox4, pcox1, pcox2, pcox3, rsf1, rsf2, mtlr1, dnnsurv1,coxboost,gacox,gamtlr,gacoxboost,limmamtlr,limmacoxboost,survivalsvm,deepsurv,deephit)usfull=want[[1]]
g29=want[[2]]
g30=want[[3]]
g31=want[[4]]
g32=want[[5]]
g33=want[[6]]
g34=want[[7]]
g35=want[[8]]
#ggsave(plot=g34,file="us_auc.pdf",device = "pdf")
#characteristic_table(rsf1)[[1]]
characteristic_table(pcox3)[[1]]## [1] 1
empty_list=list()
for(i in 1:50){
empty_list[[i]]=data.frame(matrix(NA, ncol = 26, nrow = 5))
colnames(empty_list[[i]])=c("hc_acc5", "bc_acc5", "unoc_acc5" ,"ghc_acc5" , "bs1" , "bs2" ,"bs3","bs4", "bs5", "bs6" , "auc1" , "auc2","auc3" , "auc4","auc5" , "auc6", "auc7", "auc8", "auc9", "auc10" , "auc11" , "auc12" , "auc13", "auc14" , "auc15" , "auc")
}
cox1=readRDS("savedresults/melanomaclinical_cox1.rds")
cox2=empty_list
cox3=empty_list
cox4=empty_list
#pcox1=readRDS("US/cox4.rds")
pcox1=readRDS("savedresults/melanomaclinical_p_cox1.rds")
pcox2=readRDS("savedresults/melanomaclinical_p_cox2.rds")
pcox3=readRDS("savedresults/melanomaclinical_p_cox3.rds")
rsf1=readRDS("savedresults/melanomaclinical_rsf1.rds")
rsf2=readRDS("savedresults/melanomaclinical_rsf2.rds")
mtlr1=readRDS("savedresults/melanomaclinical_mtlr.rds")
#dnnsurv1=readRDS("savedresults/melanomaclinical_dnnsurv.rds")
dnnsurv1=empty_list
# summ=0
# for (i in 1:length(dnnsurv1)){
# if (class(dnnsurv1[[i]])=="try-error"){
# summ=summ+1
# dnnsurv1[[i]]=data.frame(matrix(NA, ncol = 26, nrow = 5))}else{cox2[[i]]=cox2[[i]]}
# colnames(dnnsurv1[[i]])=c("hc", "bc", "unoc" ,"ghc" , "br1" , "br2" ,"br3","br4", "br5", "br6" , "a1" , "a2","a3" , "a4","a5" , "a6", "a7", "a8", "a9", "a10" , "a11" , "a12" , "a13", "a14" , "a15" , "a")}
# print(summ)
coxboost=readRDS("savedresults/melanomaclinica_coxboost.rds")
gacox=readRDS("savedresults/melanomaclinical_ga_cox1.rds")
gamtlr=readRDS("savedresults/melanomaclinical_ga_mtlr.rds")
gacoxboost=readRDS("savedresults/melanomaclinical_ga_coxboost.rds")
limmamtlr=readRDS("savedresults/melanomaclinical_limma_mtlr.rds")
limmacoxboost=readRDS("savedresults/melanomaclinical_limma_coxboost.rds")
survivalsvm=readRDS("savedresults/clinical_survivalsvm.rds")
deepsurv=readRDS("savedresults/melanomaclinical_deepsurv.rds")
deephit=readRDS("savedresults/melanomaclinical_deephit.rds")
want=plot_fun(cox1, cox2, cox3, cox4, pcox1, pcox2, pcox3, rsf1, rsf2, mtlr1, dnnsurv1,coxboost,gacox,gamtlr,gacoxboost,limmamtlr,limmacoxboost,survivalsvm,deepsurv,deephit)empty_list=list()
for(i in 1:50){
empty_list[[i]]=data.frame(matrix(NA, ncol = 26, nrow = 5))
colnames(empty_list[[i]])=c("hc_acc5", "bc_acc5", "unoc_acc5" ,"ghc_acc5" , "bs1" , "bs2" ,"bs3","bs4", "bs5", "bs6" , "auc1" , "auc2","auc3" , "auc4","auc5" , "auc6", "auc7", "auc8", "auc9", "auc10" , "auc11" , "auc12" , "auc13", "auc14" , "auc15" , "auc")
}
cox1=empty_list
cox2=empty_list
cox3=empty_list
cox4=empty_list
# cox2=readRDS("melanomaitraq2/melanomaitraq_bw_cox1.rds")
# summ=0
# for (i in 1:length(cox2)){
# if (class(cox2[[i]])=="try-error"){
# summ=summ+1
# cox2[[i]]=data.frame(matrix(NA, ncol = 26, nrow = 5))}else{cox2[[i]]=cox2[[i]]}
# colnames(cox2[[i]])=c("hc_acc5", "bc_acc5", "unoc_acc5" ,"ghc_acc5" , "bs1" , "bs2" ,"bs3","bs4", "bs5", "bs6" , "auc1" , "auc2","auc3" , "auc4","auc5" , "auc6", "auc7", "auc8", "auc9", "auc10" , "auc11" , "auc12" , "auc13", "auc14" , "auc15" , "auc")}
# print(summ)
# cox3=readRDS("melanomaitraq2/melanomaitraq_bw_cox2.rds")
# summ=0
# for (i in 1:length(cox3)){
# if (class(cox3[[i]])=="try-error"){
# summ=summ+1
# cox3[[i]]=data.frame(matrix(NA, ncol = 26, nrow = 5))}else{cox2[[i]]=cox2[[i]]}
# colnames(cox3[[i]])=c("hc_acc5", "bc_acc5", "unoc_acc5" ,"ghc_acc5" , "bs1" , "bs2" ,"bs3","bs4", "bs5", "bs6" , "auc1" , "auc2","auc3" , "auc4","auc5" , "auc6", "auc7", "auc8", "auc9", "auc10" , "auc11" , "auc12" , "auc13", "auc14" , "auc15" , "auc")}
# print(summ)
# cox4=readRDS("melanomaitraq2/melanomaitraq_bw_cox3.rds")
# summ=0
# for (i in 1:length(cox4)){
# if (class(cox4[[i]])=="try-error"){
# summ=summ+1
# cox4[[i]]=data.frame(matrix(NA, ncol = 26, nrow = 5))}else{cox2[[i]]=cox2[[i]]}
# colnames(cox4[[i]])=c("hc_acc5", "bc_acc5", "unoc_acc5" ,"ghc_acc5" , "bs1" , "bs2" ,"bs3","bs4", "bs5", "bs6" , "auc1" , "auc2","auc3" , "auc4","auc5" , "auc6", "auc7", "auc8", "auc9", "auc10" , "auc11" , "auc12" , "auc13", "auc14" , "auc15" , "auc")}
# print(summ)
#pcox1=readRDS("US/cox4.rds")
pcox1=readRDS("savedresults/melanomaitraqv2_p_cox1.rds")
pcox2=empty_list
pcox3=empty_list
rsf1=readRDS("savedresults/melanomaitraqv2_rsf3.rds")
rsf2=readRDS("savedresults/melanomaitraqv2_rsf4.rds")
#mtlr1=readRDS("melanomaitraq2/melanomaitraq_mtlr.rds")
#can no longer run
mtlr1=empty_list
dnnsurv1=readRDS("savedresults/melanomaitraqv2_dnnsurv.rds")
# rsf1=rsf1$value
# rsf2=rsf2$value
# mtlr1=mtlr1$value
coxboost=readRDS("savedresults/melanomaitraqv2_coxboost.rds")
gacox=readRDS("savedresults/itraqv2_ga_cox1.rds")
gamtlr=readRDS("savedresults/melanomaitraqv2_ga_mtlr.rds")
gacoxboost=readRDS("savedresults/melanomaitraqv2_ga_coxboost.rds")
limmamtlr=readRDS("savedresults/melanomaitraqv2_limma_mtlr.rds")
limmacoxboost=readRDS("savedresults/melanomaitraqv2_limma_coxboost.rds")
survivalsvm=readRDS("savedresults/itraqv2_survivalsvm.rds")
deepsurv=readRDS("savedresults/melanomaitraqv2_deepsurv.rds")
deephit=readRDS("savedresults/melanomaitraqv2_deephit.rds")
want=plot_fun(cox1, cox2, cox3, cox4, pcox1, pcox2, pcox3, rsf1, rsf2, mtlr1, dnnsurv1,coxboost,gacox,gamtlr,gacoxboost,limmamtlr,limmacoxboost,survivalsvm,deepsurv,deephit)melanoma_itraqfull=want[[1]]
g43=want[[2]]
g44=want[[3]]
g45=want[[4]]
g46=want[[5]]
g47=want[[6]]
g48=want[[7]]
g49=want[[8]]
#ggsave(plot=g48,file="itraq_auc.pdf",device = "pdf")
#characteristic_table(rsf1)[[2]]
characteristic_table(dnnsurv1)[[1]]## [1] 0.63
empty_list=list()
for(i in 1:50){
empty_list[[i]]=data.frame(matrix(NA, ncol = 26, nrow = 5))
colnames(empty_list[[i]])=c("hc_acc5", "bc_acc5", "unoc_acc5" ,"ghc_acc5" , "bs1" , "bs2" ,"bs3","bs4", "bs5", "bs6" , "auc1" , "auc2","auc3" , "auc4","auc5" , "auc6", "auc7", "auc8", "auc9", "auc10" , "auc11" , "auc12" , "auc13", "auc14" , "auc15" , "auc")
}
cox1=empty_list
cox2=readRDS("savedresults/melanomananov3_bw_cox1.rds")
summ=0
for (i in 1:length(cox2)){
if (class(cox2[[i]])=="try-error"){
summ=summ+1
cox2[[i]]=data.frame(matrix(NA, ncol = 26, nrow = 5))}else{cox2[[i]]=cox2[[i]]}
colnames(cox2[[i]])=c("hc_acc5", "bc_acc5", "unoc_acc5" ,"ghc_acc5" , "bs1" , "bs2" ,"bs3","bs4", "bs5", "bs6" , "auc1" , "auc2","auc3" , "auc4","auc5" , "auc6", "auc7", "auc8", "auc9", "auc10" , "auc11" , "auc12" , "auc13", "auc14" , "auc15" , "auc")}
print(summ)## [1] 16
cox3=readRDS("savedresults/melanomananov3_bw_cox2.rds")
summ=0
for (i in 1:length(cox3)){
if (class(cox3[[i]])=="try-error"){
summ=summ+1
cox3[[i]]=data.frame(matrix(NA, ncol = 26, nrow = 5))}else{cox2[[i]]=cox2[[i]]}
colnames(cox3[[i]])=c("hc_acc5", "bc_acc5", "unoc_acc5" ,"ghc_acc5" , "bs1" , "bs2" ,"bs3","bs4", "bs5", "bs6" , "auc1" , "auc2","auc3" , "auc4","auc5" , "auc6", "auc7", "auc8", "auc9", "auc10" , "auc11" , "auc12" , "auc13", "auc14" , "auc15" , "auc")}
print(summ)## [1] 20
cox4=readRDS("savedresults/melanomananov3_bw_cox3.rds")
summ=0
for (i in 1:length(cox4)){
if (class(cox4[[i]])=="try-error"){
summ=summ+1
cox4[[i]]=data.frame(matrix(NA, ncol = 26, nrow = 5))}else{cox2[[i]]=cox2[[i]]}
colnames(cox4[[i]])=c("hc_acc5", "bc_acc5", "unoc_acc5" ,"ghc_acc5" , "bs1" , "bs2" ,"bs3","bs4", "bs5", "bs6" , "auc1" , "auc2","auc3" , "auc4","auc5" , "auc6", "auc7", "auc8", "auc9", "auc10" , "auc11" , "auc12" , "auc13", "auc14" , "auc15" , "auc")}
print(summ)## [1] 16
pcox1=readRDS("savedresults/melanomananov3_p_cox1.rds")
summ=0
for (i in 1:length(pcox1)){
if (class(pcox1[[i]])=="try-error"){
summ=summ+1
pcox1[[i]]=data.frame(matrix(NA, ncol = 26, nrow = 5))}else{pcox1[[i]]=pcox1[[i]]}
colnames(pcox1[[i]])=c("hc_acc5", "bc_acc5", "unoc_acc5" ,"ghc_acc5" , "bs1" , "bs2" ,"bs3","bs4", "bs5", "bs6" , "auc1" , "auc2","auc3" , "auc4","auc5" , "auc6", "auc7", "auc8", "auc9", "auc10" , "auc11" , "auc12" , "auc13", "auc14" , "auc15" , "auc")}
print(summ)## [1] 0
pcox2=readRDS("savedresults/melanomananov3_p_cox2.rds")
summ=0
for (i in 1:length(pcox2)){
if (class(pcox2[[i]])=="try-error"){
summ=summ+1
pcox2[[i]]=data.frame(matrix(NA, ncol = 26, nrow = 5))}else{pcox2[[i]]=pcox2[[i]]}
colnames(pcox2[[i]])=c("hc_acc5", "bc_acc5", "unoc_acc5" ,"ghc_acc5" , "bs1" , "bs2" ,"bs3","bs4", "bs5", "bs6" , "auc1" , "auc2","auc3" , "auc4","auc5" , "auc6", "auc7", "auc8", "auc9", "auc10" , "auc11" , "auc12" , "auc13", "auc14" , "auc15" , "auc")}
print(summ)## [1] 0
pcox3=readRDS("savedresults/melanomananov3_p_cox3.rds")
summ=0
for (i in 1:length(pcox3)){
if (class(pcox3[[i]])=="try-error"){
summ=summ+1
pcox3[[i]]=data.frame(matrix(NA, ncol = 26, nrow = 5))}else{pcox3[[i]]=pcox3[[i]]}
colnames(pcox3[[i]])=c("hc_acc5", "bc_acc5", "unoc_acc5" ,"ghc_acc5" , "bs1" , "bs2" ,"bs3","bs4", "bs5", "bs6" , "auc1" , "auc2","auc3" , "auc4","auc5" , "auc6", "auc7", "auc8", "auc9", "auc10" , "auc11" , "auc12" , "auc13", "auc14" , "auc15" , "auc")}
print(summ)## [1] 0
rsf1=readRDS("savedresults/melanomananov3_rsf3.rds")
summ=0
for (i in 1:length(rsf1)){
if (class(rsf1[[i]])=="try-error"){
summ=summ+1
rsf1[[i]]=data.frame(matrix(NA, ncol = 26, nrow = 5))}else{rsf1[[i]]=rsf1[[i]]}
colnames(rsf1[[i]])=c("hc_acc5", "bc_acc5", "unoc_acc5" ,"ghc_acc5" , "bs1" , "bs2" ,"bs3","bs4", "bs5", "bs6" , "auc1" , "auc2","auc3" , "auc4","auc5" , "auc6", "auc7", "auc8", "auc9", "auc10" , "auc11" , "auc12" , "auc13", "auc14" , "auc15" , "auc")}
print(summ)## [1] 0
rsf2=readRDS("savedresults/melanomananov3_rsf4.rds")
summ=0
for (i in 1:length(rsf2)){
if (class(rsf2[[i]])=="try-error"){
summ=summ+1
rsf2[[i]]=data.frame(matrix(NA, ncol = 26, nrow = 5))}else{rsf2[[i]]=rsf2[[i]]}
colnames(rsf2[[i]])=c("hc_acc5", "bc_acc5", "unoc_acc5" ,"ghc_acc5" , "bs1" , "bs2" ,"bs3","bs4", "bs5", "bs6" , "auc1" , "auc2","auc3" , "auc4","auc5" , "auc6", "auc7", "auc8", "auc9", "auc10" , "auc11" , "auc12" , "auc13", "auc14" , "auc15" , "auc")}
print(summ)## [1] 0
mtlr1=readRDS("savedresults/melanomananov3_mtlr.rds")
summ=0
for (i in 1:length(mtlr1)){
if (class(mtlr1[[i]])=="try-error"){
summ=summ+1
mtlr1[[i]]=data.frame(matrix(NA, ncol = 26, nrow = 5))}else{mtlr1[[i]]=mtlr1[[i]]}
colnames(mtlr1[[i]])=c("hc_acc5", "bc_acc5", "unoc_acc5" ,"ghc_acc5" , "bs1" , "bs2" ,"bs3","bs4", "bs5", "bs6" , "auc1" , "auc2","auc3" , "auc4","auc5" , "auc6", "auc7", "auc8", "auc9", "auc10" , "auc11" , "auc12" , "auc13", "auc14" , "auc15" , "auc")}
print(summ)## [1] 0
dnnsurv1=readRDS("savedresults/melanomananov3_dnnsurv.rds")
summ=0
for (i in 1:length(dnnsurv1)){
if (class(dnnsurv1[[i]])=="try-error"){
summ=summ+1
dnnsurv1[[i]]=data.frame(matrix(NA, ncol = 26, nrow = 5))}else{dnnsurv1[[i]]=dnnsurv1[[i]]}
colnames(dnnsurv1[[i]])=c("hc_acc5", "bc_acc5", "unoc_acc5" ,"ghc_acc5" , "bs1" , "bs2" ,"bs3","bs4", "bs5", "bs6" , "auc1" , "auc2","auc3" , "auc4","auc5" , "auc6", "auc7", "auc8", "auc9", "auc10" , "auc11" , "auc12" , "auc13", "auc14" , "auc15" , "auc")}
print(summ)## [1] 0
coxboost=readRDS("savedresults/melanomananov3_coxboost.rds")
summ=0
for (i in 1:length(coxboost)){
if (class(coxboost[[i]])=="try-error"){
summ=summ+1
coxboost[[i]]=data.frame(matrix(NA, ncol = 26, nrow = 5))}else{coxboost[[i]]=coxboost[[i]]}
colnames(coxboost[[i]])=c("hc_acc5", "bc_acc5", "unoc_acc5" ,"ghc_acc5" , "bs1" , "bs2" ,"bs3","bs4", "bs5", "bs6" , "auc1" , "auc2","auc3" , "auc4","auc5" , "auc6", "auc7", "auc8", "auc9", "auc10" , "auc11" , "auc12" , "auc13", "auc14" , "auc15" , "auc")}
print(summ)## [1] 0
gacox=readRDS("savedresults/melanomananov3_ga_cox1.rds")
gamtlr=readRDS("savedresults/melanomananov3_ga_mtlr.rds")
gacoxboost=readRDS("savedresults/melanomananov3_ga_coxboost.rds")
limmamtlr=readRDS("savedresults/melanomananov3_limma_mtlr.rds")
limmacoxboost=readRDS("savedresults/melanomananov3_limma_coxboost.rds")
deepsurv=readRDS("savedresults/melanomananov3_deepsurv.rds")
deephit=readRDS("savedresults/melanomananov3_deephit.rds")
survivalsvm=empty_list
want=plot_fun(cox1, cox2, cox3, cox4, pcox1, pcox2, pcox3, rsf1, rsf2, mtlr1, dnnsurv1,coxboost,gacox,gamtlr,gacoxboost,limmamtlr,limmacoxboost,survivalsvm,deepsurv,deephit)melanomananofull=want[[1]]
g57=want[[2]]
g58=want[[3]]
g59=want[[4]]
g60=want[[5]]
g61=want[[6]]
g62=want[[7]]
g63=want[[8]]
characteristic_table(rsf1)[[2]]## model hc bc unoc
## Length:100 Min. :0.2121 Min. : NA Min. :0.2121
## Class :character 1st Qu.:0.5942 1st Qu.: NA 1st Qu.:0.5942
## Mode :character Median :0.6970 Median : NA Median :0.6985
## Mean :0.6831 Mean :NaN Mean :0.6851
## 3rd Qu.:0.7631 3rd Qu.: NA 3rd Qu.:0.7673
## Max. :1.0000 Max. : NA Max. :1.0000
## NA's :100
## ghc bs1 bs2 bs3
## Min. : NA Min. :0.869 Min. :0.869 Min. :0.1046
## 1st Qu.: NA 1st Qu.:1.793 1st Qu.:1.793 1st Qu.:0.1441
## Median : NA Median :2.179 Median :2.179 Median :0.1819
## Mean :NaN Mean :2.340 Mean :2.340 Mean : Inf
## 3rd Qu.: NA 3rd Qu.:2.610 3rd Qu.:2.610 3rd Qu.:0.5065
## Max. : NA Max. :7.385 Max. :7.385 Max. : Inf
## NA's :100 NA's :23
## bs4 bs5 bs6 auc1
## Min. :0.1061 Min. :0.1061 Min. :0.1061 Min. :0.05556
## 1st Qu.:0.1628 1st Qu.:0.1628 1st Qu.:0.1628 1st Qu.:0.50000
## Median :0.1949 Median :0.1949 Median :0.1949 Median :0.63393
## Mean :0.1987 Mean :0.1987 Mean :0.1987 Mean :0.63224
## 3rd Qu.:0.2193 3rd Qu.:0.2193 3rd Qu.:0.2193 3rd Qu.:0.78571
## Max. :0.5065 Max. :0.5065 Max. :0.5065 Max. :1.00000
##
## auc2 auc3 auc4 auc5
## Min. :0.1500 Min. :0.1500 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.6083 1st Qu.:0.7500 1st Qu.:0.7000 1st Qu.:0.7000
## Median :0.7361 Median :0.8500 Median :0.8000 Median :0.8000
## Mean :0.7245 Mean :0.8317 Mean :0.7881 Mean :0.7881
## 3rd Qu.:0.8500 3rd Qu.:0.9458 3rd Qu.:0.9071 3rd Qu.:0.9071
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
##
## auc6 auc7 auc8 auc9
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.7000 1st Qu.:0.7000 1st Qu.:0.6667 1st Qu.:0.6823
## Median :0.8000 Median :0.8000 Median :0.8000 Median :0.8500
## Mean :0.7881 Mean :0.7881 Mean :0.7792 Mean :0.7970
## 3rd Qu.:0.9071 3rd Qu.:0.9071 3rd Qu.:0.9071 3rd Qu.:0.9444
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
##
## auc10 auc11 auc12 auc13
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.6667 1st Qu.:0.6667 1st Qu.:0.7333 1st Qu.:0.7333
## Median :0.8417 Median :0.8333 Median :0.8593 Median :0.8593
## Mean :0.7954 Mean :0.7820 Mean :0.8087 Mean :0.8087
## 3rd Qu.:0.9392 3rd Qu.:0.9375 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
##
## auc14 auc15 auc
## Min. :0.0000 Min. :0.0000 Min. :0.1479
## 1st Qu.:0.7333 1st Qu.:0.5958 1st Qu.:0.6398
## Median :0.8593 Median :0.7955 Median :0.7379
## Mean :0.8087 Mean :0.7151 Mean :0.7246
## 3rd Qu.:1.0000 3rd Qu.:0.9392 3rd Qu.:0.8197
## Max. :1.0000 Max. :1.0000 Max. :1.0000
##
## [1] 1
## model hc bc unoc
## Length:100 Min. :0.2121 Min. :0.2361 Min. :0.0000
## Class :character 1st Qu.:0.5000 1st Qu.:0.4933 1st Qu.:0.5000
## Mode :character Median :0.5917 Median :0.5694 Median :0.6000
## Mean :0.5904 Mean :0.5725 Mean :0.5871
## 3rd Qu.:0.6667 3rd Qu.:0.6528 3rd Qu.:0.6676
## Max. :0.9615 Max. :1.0000 Max. :0.9615
##
## ghc bs1 bs2 bs3 bs4
## Min. :0.0000 Min. :0.816 Min. :0.816 Min. : NA Min. : NA
## 1st Qu.:0.6086 1st Qu.:1.847 1st Qu.:1.847 1st Qu.: NA 1st Qu.: NA
## Median :0.6997 Median :2.097 Median :2.097 Median : NA Median : NA
## Mean :0.7122 Mean :2.251 Mean :2.251 Mean :NaN Mean :NaN
## 3rd Qu.:0.8111 3rd Qu.:2.586 3rd Qu.:2.586 3rd Qu.: NA 3rd Qu.: NA
## Max. :0.9653 Max. :6.249 Max. :6.249 Max. : NA Max. : NA
## NA's :100 NA's :100
## bs5 bs6 auc1 auc2 auc3
## Min. : NA Min. : NA Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.: NA 1st Qu.: NA 1st Qu.:0.3889 1st Qu.:0.3889 1st Qu.:0.5000
## Median : NA Median : NA Median :0.5556 Median :0.5528 Median :0.6500
## Mean :NaN Mean :NaN Mean :0.5521 Mean :0.5520 Mean :0.6383
## 3rd Qu.: NA 3rd Qu.: NA 3rd Qu.:0.7500 3rd Qu.:0.6875 3rd Qu.:0.8000
## Max. : NA Max. : NA Max. :1.0000 Max. :1.0000 Max. :1.0000
## NA's :100 NA's :100
## auc4 auc5 auc6 auc7
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.5000 1st Qu.:0.5000 1st Qu.:0.5000 1st Qu.:0.5000
## Median :0.6500 Median :0.6500 Median :0.6500 Median :0.6500
## Mean :0.6361 Mean :0.6361 Mean :0.6361 Mean :0.6361
## 3rd Qu.:0.7893 3rd Qu.:0.7893 3rd Qu.:0.7893 3rd Qu.:0.7893
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
##
## auc8 auc9 auc10 auc11
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.5000 1st Qu.:0.5000 1st Qu.:0.4594 1st Qu.:0.4286
## Median :0.6111 Median :0.6000 Median :0.6056 Median :0.6000
## Mean :0.6186 Mean :0.6037 Mean :0.6052 Mean :0.5931
## 3rd Qu.:0.7857 3rd Qu.:0.7857 3rd Qu.:0.7857 3rd Qu.:0.7857
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
##
## auc12 auc13 auc14 auc15
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.4667 1st Qu.:0.4667 1st Qu.:0.4667 1st Qu.:0.3989
## Median :0.6000 Median :0.6000 Median :0.6000 Median :0.5328
## Mean :0.5964 Mean :0.5964 Mean :0.5964 Mean :0.5319
## 3rd Qu.:0.7589 3rd Qu.:0.7589 3rd Qu.:0.7589 3rd Qu.:0.7500
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
##
## auc
## Min. :0.1429
## 1st Qu.:0.4679
## Median :0.5833
## Mean :0.5896
## 3rd Qu.:0.7108
## Max. :1.0000
##
## model hc bc unoc
## Length:100 Min. :0.2121 Min. :0.2361 Min. :0.2121
## Class :character 1st Qu.:0.4803 1st Qu.:0.4715 1st Qu.:0.4803
## Mode :character Median :0.6030 Median :0.5833 Median :0.6089
## Mean :0.5985 Mean :0.5765 Mean :0.5992
## 3rd Qu.:0.7000 3rd Qu.:0.6592 3rd Qu.:0.7000
## Max. :0.9333 Max. :0.8750 Max. :0.9333
##
## ghc bs1 bs2 bs3 bs4
## Min. :0.5081 Min. :0.886 Min. :0.886 Min. : NA Min. : NA
## 1st Qu.:0.6029 1st Qu.:1.767 1st Qu.:1.767 1st Qu.: NA 1st Qu.: NA
## Median :0.6761 Median :2.106 Median :2.106 Median : NA Median : NA
## Mean :0.7117 Mean :2.243 Mean :2.243 Mean :NaN Mean :NaN
## 3rd Qu.:0.8042 3rd Qu.:2.472 3rd Qu.:2.472 3rd Qu.: NA 3rd Qu.: NA
## Max. :0.9664 Max. :5.617 Max. :5.617 Max. : NA Max. : NA
## NA's :100 NA's :100
## bs5 bs6 auc1 auc2 auc3
## Min. : NA Min. : NA Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.: NA 1st Qu.: NA 1st Qu.:0.3854 1st Qu.:0.3972 1st Qu.:0.5000
## Median : NA Median : NA Median :0.5556 Median :0.5714 Median :0.6500
## Mean :NaN Mean :NaN Mean :0.5518 Mean :0.5718 Mean :0.6498
## 3rd Qu.: NA 3rd Qu.: NA 3rd Qu.:0.7500 3rd Qu.:0.7500 3rd Qu.:0.8000
## Max. : NA Max. : NA Max. :1.0000 Max. :1.0000 Max. :1.0000
## NA's :100 NA's :100
## auc4 auc5 auc6 auc7
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.5000 1st Qu.:0.5000 1st Qu.:0.5000 1st Qu.:0.5000
## Median :0.6500 Median :0.6500 Median :0.6500 Median :0.6500
## Mean :0.6484 Mean :0.6484 Mean :0.6484 Mean :0.6484
## 3rd Qu.:0.8333 3rd Qu.:0.8333 3rd Qu.:0.8333 3rd Qu.:0.8333
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
##
## auc8 auc9 auc10 auc11
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.4625 1st Qu.:0.4500 1st Qu.:0.4500 1st Qu.:0.4486
## Median :0.6500 Median :0.6270 Median :0.6583 Median :0.6464
## Mean :0.6333 Mean :0.6212 Mean :0.6233 Mean :0.6143
## 3rd Qu.:0.8333 3rd Qu.:0.8000 3rd Qu.:0.8031 3rd Qu.:0.8031
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
##
## auc12 auc13 auc14 auc15
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.4332 1st Qu.:0.4332 1st Qu.:0.4332 1st Qu.:0.3333
## Median :0.6460 Median :0.6460 Median :0.6460 Median :0.5298
## Mean :0.6112 Mean :0.6112 Mean :0.6112 Mean :0.5364
## 3rd Qu.:0.8177 3rd Qu.:0.8177 3rd Qu.:0.8177 3rd Qu.:0.7500
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
##
## auc
## Min. :0.1429
## 1st Qu.:0.4593
## Median :0.6024
## Mean :0.5992
## 3rd Qu.:0.7538
## Max. :0.9597
##
## [1] 0.8
empty_list=list()
for(i in 1:50){
empty_list[[i]]=data.frame(matrix(NA, ncol = 26, nrow = 5))
colnames(empty_list[[i]])=c("hc_acc5", "bc_acc5", "unoc_acc5" ,"ghc_acc5" , "bs1" , "bs2" ,"bs3","bs4", "bs5", "bs6" , "auc1" , "auc2","auc3" , "auc4","auc5" , "auc6", "auc7", "auc8", "auc9", "auc10" , "auc11" , "auc12" , "auc13", "auc14" , "auc15" , "auc")
}
cox1=empty_list
cox2=empty_list
cox3=empty_list
cox4=empty_list
pcox1=readRDS("savedresults/gse1_p_cox1.rds")
pcox2=readRDS("savedresults/gse1_p_cox2.rds")
pcox3=readRDS("savedresults/gse1_p_cox3.rds")
rsf1=readRDS("savedresults/gse1_rsf1.rds")
rsf2=readRDS("savedresults/gse1_rsf2.rds")
mtlr1=readRDS("savedresults/gse1_mtlr.rds")
dnnsurv1=empty_list
coxboost=readRDS("savedresults/gse1_coxboost.rds")
gacox=readRDS("savedresults/gse1_ga_cox1.rds")
gamtlr=readRDS("savedresults/gse1_ga_mtlr.rds")
gacoxboost=readRDS("savedresults/gse1_ga_coxboost.rds")
limmamtlr=readRDS("savedresults/gse1_limma_mtlr.rds")
limmacoxboost=readRDS("savedresults/gse1_limma_coxboost.rds")
survivalsvm=readRDS("savedresults/gse1_survivalsvm.rds")
deepsurv=readRDS("savedresults/gse1_deepsurv.rds")
deephit=readRDS("savedresults/gse1_deephit.rds")
want=plot_fun(cox1, cox2, cox3, cox4, pcox1, pcox2, pcox3, rsf1, rsf2, mtlr1, dnnsurv1,coxboost,gacox,gamtlr,gacoxboost,limmamtlr,limmacoxboost,survivalsvm,deepsurv,deephit)gse1full=want[[1]]
g64=want[[2]]
g65=want[[3]]
g66=want[[4]]
g67=want[[5]]
g68=want[[6]]
g69=want[[7]]
g70=want[[8]]
characteristic_table(pcox1)[[2]]## model hc bc unoc
## Length:100 Min. :0.2828 Min. :0.4326 Min. :0.0000
## Class :character 1st Qu.:0.4914 1st Qu.:0.4960 1st Qu.:0.2035
## Mode :character Median :0.5216 Median :0.5217 Median :0.4891
## Mean :0.5263 Mean :0.5850 Mean :0.4593
## 3rd Qu.:0.5798 3rd Qu.:0.5526 3rd Qu.:0.7034
## Max. :0.6675 Max. :1.0000 Max. :0.9995
##
## ghc bs1 bs2 bs3
## Min. :0.0000 Min. : 36.3 Min. : 36.3 Min. : NA
## 1st Qu.:0.5193 1st Qu.: 166.6 1st Qu.: 166.6 1st Qu.: NA
## Median :0.5555 Median : 344.9 Median : 344.9 Median : NA
## Mean :0.4960 Mean : 830.8 Mean : 830.8 Mean :NaN
## 3rd Qu.:0.6060 3rd Qu.: 930.9 3rd Qu.: 930.9 3rd Qu.: NA
## Max. :0.8125 Max. :13466.5 Max. :13466.5 Max. : NA
## NA's :100
## bs4 bs5 bs6 auc1 auc2
## Min. : NA Min. : NA Min. : NA Min. :0.2339 Min. :0.2230
## 1st Qu.: NA 1st Qu.: NA 1st Qu.: NA 1st Qu.:0.5000 1st Qu.:0.4991
## Median : NA Median : NA Median : NA Median :0.5397 Median :0.5346
## Mean :NaN Mean :NaN Mean :NaN Mean :0.5494 Mean :0.5422
## 3rd Qu.: NA 3rd Qu.: NA 3rd Qu.: NA 3rd Qu.:0.6237 3rd Qu.:0.6223
## Max. : NA Max. : NA Max. : NA Max. :0.8539 Max. :0.8386
## NA's :100 NA's :100 NA's :100
## auc3 auc4 auc5 auc6
## Min. :0.2192 Min. :0.2192 Min. :0.2192 Min. :0.1202
## 1st Qu.:0.4759 1st Qu.:0.4839 1st Qu.:0.4840 1st Qu.:0.4547
## Median :0.5028 Median :0.5089 Median :0.5259 Median :0.5000
## Mean :0.5266 Mean :0.5410 Mean :0.5501 Mean :0.5107
## 3rd Qu.:0.6022 3rd Qu.:0.6279 3rd Qu.:0.6479 3rd Qu.:0.5779
## Max. :0.7431 Max. :0.7726 Max. :0.8464 Max. :0.7812
##
## auc7 auc8 auc9 auc10
## Min. :0.1179 Min. :0.08111 Min. :0.1401 Min. :0.1401
## 1st Qu.:0.4662 1st Qu.:0.44623 1st Qu.:0.4156 1st Qu.:0.4142
## Median :0.5000 Median :0.50000 Median :0.5000 Median :0.5000
## Mean :0.5298 Mean :0.51724 Mean :0.4984 Mean :0.4956
## 3rd Qu.:0.6141 3rd Qu.:0.60407 3rd Qu.:0.5886 3rd Qu.:0.5891
## Max. :0.8143 Max. :0.80795 Max. :0.8070 Max. :0.9260
##
## auc11 auc12 auc13 auc14
## Min. :0.06721 Min. :0.07735 Min. :0.04917 Min. :0.01518
## 1st Qu.:0.36094 1st Qu.:0.39947 1st Qu.:0.36693 1st Qu.:0.36549
## Median :0.48841 Median :0.50000 Median :0.50000 Median :0.50000
## Mean :0.46363 Mean :0.48358 Mean :0.45869 Mean :0.47460
## 3rd Qu.:0.51891 3rd Qu.:0.55220 3rd Qu.:0.54834 3rd Qu.:0.55976
## Max. :0.92601 Max. :0.93106 Max. :0.93973 Max. :0.93220
##
## auc15 auc
## Min. :0.05104 Min. :0.2182
## 1st Qu.:0.47657 1st Qu.:0.4880
## Median :0.50839 Median :0.5311
## Mean :0.55389 Mean :0.5264
## 3rd Qu.:0.69182 3rd Qu.:0.5871
## Max. :0.90737 Max. :0.7237
##
empty_list=list()
for(i in 1:50){
empty_list[[i]]=data.frame(matrix(NA, ncol = 26, nrow = 5))
colnames(empty_list[[i]])=c("hc_acc5", "bc_acc5", "unoc_acc5" ,"ghc_acc5" , "bs1" , "bs2" ,"bs3","bs4", "bs5", "bs6" , "auc1" , "auc2","auc3" , "auc4","auc5" , "auc6", "auc7", "auc8", "auc9", "auc10" , "auc11" , "auc12" , "auc13", "auc14" , "auc15" , "auc")
}
cox1=empty_list
cox2=empty_list
cox3=empty_list
cox4=empty_list
pcox1=readRDS("savedresults/gse4_p_cox2.rds")
pcox2=readRDS("savedresults/gse4_p_cox2.rds")
pcox3=readRDS("savedresults/gse4_p_cox3.rds")
rsf1=readRDS("savedresults/gse4_rsf1.rds")
rsf2=readRDS("savedresults/gse4_rsf1.rds")
#mtlr1=readRDS("savedresults/gse4_mtlr.rds")
mtlr1=empty_list
dnnsurv1=empty_list
coxboost=readRDS("savedresults/gse4_coxboost.rds")
gacox=readRDS("savedresults/gse4_ga_cox1.rds")
gamtlr=readRDS("savedresults/gse4_ga_mtlr.rds")
gacoxboost=readRDS("savedresults/gse4_ga_coxboost.rds")
limmamtlr=readRDS("savedresults/gse4_limma_mtlr.rds")
limmacoxboost=readRDS("savedresults/gse4_limma_coxboost.rds")
survivalsvm=readRDS("savedresults/gse4_survivalsvm.rds")
deepsurv=empty_list
deephit=empty_list
want=plot_fun(cox1, cox2, cox3, cox4, pcox1, pcox2, pcox3, rsf1, rsf2, mtlr1, dnnsurv1,coxboost,gacox,gamtlr,gacoxboost,limmamtlr,limmacoxboost,survivalsvm,deepsurv,deephit)gse2full=want[[1]]
g71=want[[2]]
g72=want[[3]]
g73=want[[4]]
g74=want[[5]]
g75=want[[6]]
g76=want[[7]]
g77=want[[8]]
characteristic_table(rsf1)[[2]]## model hc bc unoc
## Length:100 Min. :0.1471 Min. : NA Min. :0.0000
## Class :character 1st Qu.:0.4808 1st Qu.: NA 1st Qu.:0.0000
## Mode :character Median :0.5000 Median : NA Median :0.0000
## Mean :0.4739 Mean :NaN Mean :0.1718
## 3rd Qu.:0.5000 3rd Qu.: NA 3rd Qu.:0.3775
## Max. :0.7000 Max. : NA Max. :0.7092
## NA's :100
## ghc bs1 bs2 bs3 bs4
## Min. : NA Min. :1.333 Min. :1.333 Min. : NA Min. : NA
## 1st Qu.: NA 1st Qu.:2.108 1st Qu.:2.108 1st Qu.: NA 1st Qu.: NA
## Median : NA Median :2.569 Median :2.569 Median : NA Median : NA
## Mean :NaN Mean :2.648 Mean :2.648 Mean :NaN Mean :NaN
## 3rd Qu.: NA 3rd Qu.:3.022 3rd Qu.:3.022 3rd Qu.: NA 3rd Qu.: NA
## Max. : NA Max. :4.360 Max. :4.360 Max. : NA Max. : NA
## NA's :100 NA's :100 NA's :100
## bs5 bs6 auc1 auc2
## Min. :0.1619 Min. :0.1619 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.1929 1st Qu.:0.1929 1st Qu.:0.4501 1st Qu.:0.5000
## Median :0.2138 Median :0.2138 Median :0.5000 Median :0.5000
## Mean :0.2188 Mean :0.2188 Mean :0.4559 Mean :0.4641
## 3rd Qu.:0.2338 3rd Qu.:0.2338 3rd Qu.:0.5000 3rd Qu.:0.5000
## Max. :0.3140 Max. :0.3140 Max. :0.7500 Max. :0.8125
##
## auc3 auc4 auc5 auc6
## Min. :0.05556 Min. :0.05556 Min. :0.0625 Min. :0.0000
## 1st Qu.:0.50000 1st Qu.:0.50000 1st Qu.:0.4910 1st Qu.:0.4600
## Median :0.50000 Median :0.50000 Median :0.5000 Median :0.5000
## Mean :0.48997 Mean :0.48114 Mean :0.4593 Mean :0.4497
## 3rd Qu.:0.50000 3rd Qu.:0.50000 3rd Qu.:0.5000 3rd Qu.:0.5000
## Max. :0.81250 Max. :0.81250 Max. :0.8333 Max. :0.8333
##
## auc7 auc8 auc9 auc10
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.4305 1st Qu.:0.4305 1st Qu.:0.4305 1st Qu.:0.4305
## Median :0.5000 Median :0.5000 Median :0.5000 Median :0.5000
## Mean :0.4445 Mean :0.4445 Mean :0.4445 Mean :0.4465
## 3rd Qu.:0.5000 3rd Qu.:0.5000 3rd Qu.:0.5000 3rd Qu.:0.5000
## Max. :0.6764 Max. :0.6764 Max. :0.6764 Max. :0.7500
##
## auc11 auc12 auc13 auc14
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.4305 1st Qu.:0.4936 1st Qu.:0.4340 1st Qu.:0.4323
## Median :0.5000 Median :0.5000 Median :0.5000 Median :0.5000
## Mean :0.4512 Mean :0.4667 Mean :0.4518 Mean :0.4573
## 3rd Qu.:0.5000 3rd Qu.:0.5000 3rd Qu.:0.5000 3rd Qu.:0.5000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
##
## auc15 auc
## Min. :0.0000 Min. :0.0795
## 1st Qu.:0.3932 1st Qu.:0.4435
## Median :0.5000 Median :0.5000
## Mean :0.4438 Mean :0.4588
## 3rd Qu.:0.5000 3rd Qu.:0.5000
## Max. :1.0000 Max. :0.6883
##
empty_list=list()
for(i in 1:50){
empty_list[[i]]=data.frame(matrix(NA, ncol = 26, nrow = 5))
colnames(empty_list[[i]])=c("hc_acc5", "bc_acc5", "unoc_acc5" ,"ghc_acc5" , "bs1" , "bs2" ,"bs3","bs4", "bs5", "bs6" , "auc1" , "auc2","auc3" , "auc4","auc5" , "auc6", "auc7", "auc8", "auc9", "auc10" , "auc11" , "auc12" , "auc13", "auc14" , "auc15" , "auc")
}
cox1=empty_list
cox2=empty_list
cox3=empty_list
cox4=empty_list
pcox1=readRDS("savedresults/ngse1_p_cox1.rds")
pcox2=readRDS("savedresults/ngse1_p_cox2.rds")
pcox3=readRDS("savedresults/ngse1_p_cox3.rds")
rsf1=readRDS("savedresults/ngse1_rsf1.rds")
rsf2=readRDS("savedresults/ngse1_rsf1.rds")
mtlr1=readRDS("savedresults/ngse1_mtlr.rds")
dnnsurv1=readRDS("savedresults/ngse1_dnnsurv.rds")
coxboost=readRDS("savedresults/ngse1_coxboost.rds")
gacox=readRDS("savedresults/ngse1_ga_cox1.rds")
gamtlr=readRDS("savedresults/ngse1_ga_mtlr.rds")
gacoxboost=readRDS("savedresults/ngse1_ga_cocboost.rds")
limmamtlr=readRDS("savedresults/ngse1_limma_mtlr.rds")
#limmacoxboost=readRDS("NGSE/ngse1_limma_coxboost.rds")
limmacoxboost=empty_list
survivalsvm=readRDS("savedresults/ngse1_survivalsvm.rds")
deepsurv=empty_list
deephit=empty_list
want=plot_fun(cox1, cox2, cox3, cox4, pcox1, pcox2, pcox3, rsf1, rsf2, mtlr1, dnnsurv1,coxboost,gacox,gamtlr,gacoxboost,limmamtlr,limmacoxboost,survivalsvm,deepsurv,deephit)ngse1full=want[[1]]
g85=want[[2]]
g86=want[[3]]
g87=want[[4]]
g88=want[[5]]
g89=want[[6]]
g90=want[[7]]
g91=want[[8]]
characteristic_table(dnnsurv1)[[1]]## [1] 0.95
empty_list=list()
for(i in 1:50){
empty_list[[i]]=data.frame(matrix(NA, ncol = 26, nrow = 5))
colnames(empty_list[[i]])=c("hc_acc5", "bc_acc5", "unoc_acc5" ,"ghc_acc5" , "bs1" , "bs2" ,"bs3","bs4", "bs5", "bs6" , "auc1" , "auc2","auc3" , "auc4","auc5" , "auc6", "auc7", "auc8", "auc9", "auc10" , "auc11" , "auc12" , "auc13", "auc14" , "auc15" , "auc")
}
cox1=empty_list
cox2=empty_list
cox3=empty_list
cox4=empty_list
pcox1=readRDS("savedresults/ngse2_p_cox1.rds")
pcox2=readRDS("savedresults/ngse2_p_cox2.rds")
pcox3=readRDS("savedresults/ngse2_p_cox3.rds")
rsf1=readRDS("savedresults/ngse2_rsf1.rds")
rsf2=readRDS("savedresults/ngse2_rsf1.rds")
mtlr1=readRDS("savedresults/ngse2_mtlr.rds")
dnnsurv1=readRDS("savedresults/ngse2_dnnsurv.rds")
coxboost=readRDS("savedresults/ngse2_coxboost.rds")
gacox=readRDS("savedresults/ngse2_ga_cox1.rds")
gamtlr=readRDS("savedresults/ngse2_ga_mtlr.rds")
gacoxboost=readRDS("savedresults/ngse2_ga_cocboost.rds")
limmamtlr=readRDS("savedresults/ngse2_limma_mtlr.rds")
limmacoxboost=readRDS("savedresults/ngse2_limma_coxboost.rds")
survivalsvm=readRDS("savedresults/ngse2_survivalsvm.rds")
deepsurv=readRDS("savedresults/ngse2_deepsurv.rds")
deephit=readRDS("savedresults/ngse2_deephit.rds")
want=plot_fun(cox1, cox2, cox3, cox4, pcox1, pcox2, pcox3, rsf1, rsf2, mtlr1, dnnsurv1,coxboost,gacox,gamtlr,gacoxboost,limmamtlr,limmacoxboost,survivalsvm,deepsurv,deephit)ngse2full=want[[1]]
g92=want[[2]]
g93=want[[3]]
g94=want[[4]]
g95=want[[5]]
g96=want[[6]]
g97=want[[7]]
g98=want[[8]]
characteristic_table(dnnsurv1)[[1]]## [1] 0.98
empty_list=list()
for(i in 1:50){
empty_list[[i]]=data.frame(matrix(NA, ncol = 26, nrow = 5))
colnames(empty_list[[i]])=c("hc_acc5", "bc_acc5", "unoc_acc5" ,"ghc_acc5" , "bs1" , "bs2" ,"bs3","bs4", "bs5", "bs6" , "auc1" , "auc2","auc3" , "auc4","auc5" , "auc6", "auc7", "auc8", "auc9", "auc10" , "auc11" , "auc12" , "auc13", "auc14" , "auc15" , "auc")
}
cox1=empty_list
cox2=empty_list
cox3=empty_list
cox4=empty_list
pcox1=readRDS("savedresults/ngse3_p_cox1.rds")
pcox2=readRDS("savedresults/ngse3_p_cox2.rds")
pcox3=readRDS("savedresults/ngse3_p_cox3.rds")
rsf1=readRDS("savedresults/ngse3_rsf1.rds")
rsf2=readRDS("savedresults/ngse3_rsf1.rds")
mtlr1=readRDS("savedresults/ngse3_mtlr.rds")
dnnsurv1=readRDS("savedresults/ngse3_dnnsurv.rds")
coxboost=readRDS("savedresults/ngse3_coxboost.rds")
gacox=readRDS("savedresults/ngse3_ga_cox1.rds")
gamtlr=readRDS("savedresults/ngse3_ga_mtlr.rds")
gacoxboost=readRDS("savedresults/ngse3_ga_cocboost.rds")
limmamtlr=readRDS("savedresults/ngse3_limma_mtlr.rds")
limmacoxboost=readRDS("savedresults/ngse3_limma_coxboost.rds")
survivalsvm=empty_list
deepsurv=readRDS("savedresults/ngse3_deepsurv.rds")
deephit=readRDS("savedresults/ngse3_deephit.rds")
want=plot_fun(cox1, cox2, cox3, cox4, pcox1, pcox2, pcox3, rsf1, rsf2, mtlr1, dnnsurv1,coxboost,gacox,gamtlr,gacoxboost,limmamtlr,limmacoxboost,survivalsvm,deepsurv,deephit)ngse3full=want[[1]]
g99=want[[2]]
g100=want[[3]]
g101=want[[4]]
g102=want[[5]]
g103=want[[6]]
g104=want[[7]]
g105=want[[8]]
characteristic_table(dnnsurv1)[[1]]## [1] 0.97
empty_list=list()
for(i in 1:50){
empty_list[[i]]=data.frame(matrix(NA, ncol = 26, nrow = 5))
colnames(empty_list[[i]])=c("hc_acc5", "bc_acc5", "unoc_acc5" ,"ghc_acc5" , "bs1" , "bs2" ,"bs3","bs4", "bs5", "bs6" , "auc1" , "auc2","auc3" , "auc4","auc5" , "auc6", "auc7", "auc8", "auc9", "auc10" , "auc11" , "auc12" , "auc13", "auc14" , "auc15" , "auc")
}
cox1=empty_list
cox2=empty_list
cox3=empty_list
cox4=empty_list
pcox1=readRDS("savedresults/ngse4_p_cox1.rds")
pcox2=readRDS("savedresults/ngse4_p_cox2.rds")
pcox3=readRDS("savedresults/ngse4_p_cox3.rds")
rsf1=readRDS("savedresults/ngse4_rsf1.rds")
rsf2=readRDS("savedresults/ngse4_rsf1.rds")
mtlr1=readRDS("savedresults/ngse4_mtlr.rds")
dnnsurv1=readRDS("savedresults/ngse4_dnnsurv.rds")
coxboost=readRDS("savedresults/ngse4_coxboost.rds")
gacox=readRDS("savedresults/ngse4_ga_cox1.rds")
gamtlr=readRDS("savedresults/ngse4_ga_mtlr.rds")
gacoxboost=readRDS("savedresults/ngse4_ga_cocboost.rds")
limmamtlr=readRDS("savedresults/ngse4_limma_mtlr.rds")
limmacoxboost=readRDS("savedresults/ngse4_limma_coxboost.rds")
survivalsvm=readRDS("savedresults/ngse4_survivalsvm.rds")
deepsurv=readRDS("savedresults/ngse4_deepsurv.rds")
deephit=readRDS("savedresults/ngse4_deephit.rds")
want=plot_fun(cox1, cox2, cox3, cox4, pcox1, pcox2, pcox3, rsf1, rsf2, mtlr1, dnnsurv1,coxboost,gacox,gamtlr,gacoxboost,limmamtlr,limmacoxboost,survivalsvm,deepsurv,deephit)ngse4full=want[[1]]
g106=want[[2]]
g107=want[[3]]
g108=want[[4]]
g109=want[[5]]
g110=want[[6]]
g111=want[[7]]
g112=want[[8]]
characteristic_table(dnnsurv1)[[1]]## [1] 1
empty_list=list()
for(i in 1:50){
empty_list[[i]]=data.frame(matrix(NA, ncol = 26, nrow = 5))
colnames(empty_list[[i]])=c("hc_acc5", "bc_acc5", "unoc_acc5" ,"ghc_acc5" , "bs1" , "bs2" ,"bs3","bs4", "bs5", "bs6" , "auc1" , "auc2","auc3" , "auc4","auc5" , "auc6", "auc7", "auc8", "auc9", "auc10" , "auc11" , "auc12" , "auc13", "auc14" , "auc15" , "auc")
}
cox1=empty_list
cox2=empty_list
cox3=empty_list
cox4=empty_list
pcox1=readRDS("savedresults/ngse5_p_cox1.rds")
pcox2=readRDS("savedresults/ngse5_p_cox2.rds")
pcox3=readRDS("savedresults/ngse5_p_cox3.rds")
rsf1=readRDS("savedresults/ngse5_rsf1.rds")
rsf2=readRDS("savedresults/ngse5_rsf1.rds")
mtlr1=readRDS("savedresults/ngse5_mtlr.rds")
dnnsurv1=readRDS("savedresults/ngse5_dnnsurv.rds")
coxboost=readRDS("savedresults/ngse5_coxboost.rds")
gacox=readRDS("savedresults/ngse5_ga_cox1.rds")
gamtlr=readRDS("savedresults/ngse5_ga_mtlr.rds")
gacoxboost=readRDS("savedresults/ngse5_ga_cocboost.rds")
limmamtlr=readRDS("savedresults/ngse5_limma_mtlr.rds")
limmacoxboost=readRDS("savedresults/ngse5_limma_coxboost.rds")
survivalsvm=readRDS("savedresults/ngse5_survivalsvm.rds")
deepsurv=readRDS("savedresults/ngse5_deepsurv.rds")
deephit=readRDS("savedresults/ngse5_deephit.rds")
want=plot_fun(cox1, cox2, cox3, cox4, pcox1, pcox2, pcox3, rsf1, rsf2, mtlr1, dnnsurv1,coxboost,gacox,gamtlr,gacoxboost,limmamtlr,limmacoxboost,survivalsvm,deepsurv,deephit)ngse5full=want[[1]]
g113=want[[2]]
g114=want[[3]]
g115=want[[4]]
g116=want[[5]]
g117=want[[6]]
g118=want[[7]]
g119=want[[8]]
characteristic_table(dnnsurv1)[[1]]## [1] 1
empty_list=list()
for(i in 1:50){
empty_list[[i]]=data.frame(matrix(NA, ncol = 26, nrow = 5))
colnames(empty_list[[i]])=c("hc_acc5", "bc_acc5", "unoc_acc5" ,"ghc_acc5" , "bs1" , "bs2" ,"bs3","bs4", "bs5", "bs6" , "auc1" , "auc2","auc3" , "auc4","auc5" , "auc6", "auc7", "auc8", "auc9", "auc10" , "auc11" , "auc12" , "auc13", "auc14" , "auc15" , "auc")
}
cox1=empty_list
cox2=empty_list
cox3=empty_list
cox4=empty_list
pcox1=readRDS("savedresults/ngse6_p_cox1.rds")
pcox2=readRDS("savedresults/ngse6_p_cox2.rds")
pcox3=readRDS("savedresults/ngse6_p_cox3.rds")
rsf1=readRDS("savedresults/ngse6_rsf1.rds")
rsf2=readRDS("savedresults/ngse6_rsf1.rds")
mtlr1=readRDS("savedresults/ngse6_mtlr.rds")
dnnsurv1=readRDS("savedresults/ngse6_dnnsurv.rds")
coxboost=readRDS("savedresults/ngse6_coxboost.rds")
gacox=readRDS("savedresults/ngse6_ga_cox1.rds")
gamtlr=readRDS("savedresults/ngse6_ga_mtlr.rds")
gacoxboost=readRDS("savedresults/ngse6_ga_cocboost.rds")
limmamtlr=readRDS("savedresults/ngse6_limma_mtlr.rds")
limmacoxboost=readRDS("savedresults/ngse6_limma_coxboost.rds")
survivalsvm=readRDS("savedresults/ngse6_survivalsvm.rds")
deepsurv=readRDS("savedresults/ngse6_deepsurv.rds")
deephit=readRDS("savedresults/ngse6_deephit.rds")
want=plot_fun(cox1, cox2, cox3, cox4, pcox1, pcox2, pcox3, rsf1, rsf2, mtlr1, dnnsurv1,coxboost,gacox,gamtlr,gacoxboost,limmamtlr,limmacoxboost,survivalsvm,deepsurv,deephit)ngse6full=want[[1]]
g120=want[[2]]
g121=want[[3]]
g122=want[[4]]
g123=want[[5]]
g124=want[[6]]
g125=want[[7]]
g126=want[[8]]
characteristic_table(dnnsurv1)[[1]]## [1] 1
labelss<-c("pbc","veteran","lung","anz","us","melanoma_clinical","melanoma_itraq","melanoma_nano","gse1","gse2","gene1","gene2","gene3","gene4","gene5","gene6")
ggarrange(g1,g8,g15,g22,g29,g36,g43,g57,g64,g71,g85,g92,g99,g106,g113,g120,labels = labelss,ncol = 3, nrow = 6) #miss g78 for gse3plotdt=read_excel("result.xlsx",sheet = 1)
plotdt=as.data.frame(plotdt)
rownames(plotdt)=plotdt$method
#pheatmap(as.matrix(plotdt[,-1]),cluster_rows = FALSE,cluster_cols = TRUE)
#col_fun = colorRamp2(c( 0, 1), c("seagreen", "lightpink"))
nb.cols <- 18
mycolors <- colorRampPalette(brewer.pal(8, "RdBu"))(nb.cols)
mat=as.matrix(plotdt[,-1])
Heatmap(mat, name = "index", col = mycolors,cluster_rows = FALSE,rect_gp = gpar(col = "white", lwd = 2))#manually save heatmap here
indicator=read_excel("result.xlsx",sheet = 11)
pheatmap(indicator[,-1],cluster_cols = FALSE, cluster_rows = FALSE,color = c("orange","purple","pink"))plotdt=read_excel("result.xlsx",sheet = 7)
plotdt=as.data.frame(plotdt)
rownames(plotdt)=plotdt$method
#pheatmap(as.matrix(plotdt[,-1]),cluster_rows = FALSE,cluster_cols = TRUE)
# col_fun = colorRamp2(c( 0, 1), c("blue", "red"))
mat=as.matrix(plotdt[,-1])
Heatmap(mat, name = "index", col = mycolors,cluster_rows = FALSE,rect_gp = gpar(col = "white", lwd = 2))data_list=list(anzfull,usfull,veteranfull,lungfull,pbcfull,melanoma_clinicalfull,melanoma_itraqfull,melanomananofull,gse1full,gse2full,ngse1full,ngse2full,ngse3full,ngse4full,ngse5full,ngse6full)
datafull=bind_rows(data_list, .id = "datasets")
nb.cols <- 20
mycolors <- colorRampPalette(brewer.pal(8, "RdBu"))(nb.cols)
plotdt=datafull[datafull$model=="rsf2",]
plotdt=plotdt[plotdt$variable=="bs5",]
plotdt2=plotdt[!is.infinite(plotdt$value),]
#plotdt2$observations=as.numeric(mapvalues(plotdt2$datasets, from = seq(1,11,1), to = c(3323,3000,137,228,312,88,41,70,45,194,58)))
#plotdt2=plotdt2 %>% arrange(observations)
#plotdt$datasets=factor(plotdt$datasets,levels=c("anzfull","usfull","veteranfull","lungfull","ovarianfull","melanoma_clinicalfull","melanoma_itraqfull","melanoma_swathfull","ovarian1full","ovarian2full"))
plotdt2$datasets=mapvalues(plotdt2$datasets, from = seq(1,16,1), to = c("anz3323","us3000","veteran137","lung228","pbc312","melanoma_clinical88","melanoma_itraq41","melanoma_nano45","gse1 194","gse2 58","ngene1 115","ngene2 295","mgene3 86","ngene4 116","ngene5 78","ngene6 240"))
plotdt2$datasets=factor(plotdt2$datasets,levels=c("melanoma_itraq41","melanoma_nano45","gse2 58","ngene5 78","mgene3 86","ngene1 115","ngene4 116","gse1 194","ngene6 240","ngene2 295","melanoma_clinical88","veteran137","lung228","pbc312","us3000","anz3323"))
p=ggplot(plotdt2, aes(x=datasets, y=value, fill=datasets)) + geom_boxplot()+scale_y_continuous(limits=c(0,0.25))+ scale_fill_manual(values = mycolors)+theme_bw()
#ggsave(plot = p,file="figures/figure3c1.pdf",device = "pdf")
plotdt=datafull[datafull$model=="coxboost",]
plotdt=plotdt[plotdt$variable=="hc",]
plotdt2=plotdt[!is.infinite(plotdt$value),]
#plotdt$datasets=factor(plotdt$datasets,levels=c("anzfull","usfull","veteranfull","lungfull","ovarianfull","melanoma_clinicalfull","melanoma_itraqfull","melanoma_swathfull","ovarian1full","ovarian2full"))
plotdt2$datasets=mapvalues(plotdt2$datasets, from = seq(1,16,1), to = c("anz3323","us3000","veteran137","lung228","pbc312","melanoma_clinical88","melanoma_itraq41","melanoma_nano45","gse1 194","gse2 58","ngene1 115","ngene2 295","mgene3 86","ngene4 116","ngene5 78","ngene6 240"))
plotdt2$datasets=factor(plotdt2$datasets,levels=c("melanoma_itraq41","melanoma_nano45","gse2 58","ngene5 78","mgene3 86","ngene1 115","ngene4 116","gse1 194","ngene6 240","ngene2 295","melanoma_clinical88","veteran137","lung228","pbc312","us3000","anz3323"))
p=ggplot(plotdt2, aes(x=datasets, y=value, fill=datasets)) + geom_boxplot()+scale_y_continuous(limits=c(0,1))+ scale_fill_manual(values = mycolors)+theme_bw()
pplotdt=datafull[datafull$variable=="hc",]
plotdt2 <- plotdt %>%dplyr::group_by(datasets,model) %>%dplyr::summarize(Mean = mean(value, na.rm=TRUE))
plotdt2$datasets=plyr::mapvalues(plotdt2$datasets, from = seq(1,16,1), to = c("anz","us","veteran","lung","pbc","melanoma_clinical","melanoma_itraq","melanoma_nano","gse1","gse2","ngene1","ngene2","mgene3","ngene4","ngene5","ngene6"))
plotdt2$datasets=factor(plotdt2$datasets,levels=c("anz","us","veteran","lung","pbc","melanoma_clinical","melanoma_itraq","melanoma_nano","gse1","gse2","ngene1","ngene2","mgene3","ngene4","ngene5","ngene6"))
plotdt2$model=factor(plotdt2$model,levels=c("cox1","cox2","cox3","cox4","pcox1","pcox2","pcox3","coxboost","gacox","gacoxboost","limmacoxboost","mtlr1","gamtlr","limmamtlr","rsf1","rsf2","survivalsvm","deephit","deepsurv","dnnsurv1"))
mat=tidyr::spread(plotdt2, model, Mean)
mat=as.data.frame(mat)
rownames(mat)=mat$datasets
mat=as.matrix(mat[,-1])
nb.cols <- 8
mycolors <- colorRampPalette(rev(brewer.pal(8, "RdBu")))(nb.cols)
Heatmap(t(mat), name = "index", cluster_rows = FALSE,cluster_columns = FALSE,rect_gp = gpar(col = "white", lwd = 2),col = mycolors)#1:anz
#16:ngene5
plotdt=datafull[datafull$model%in% c("pcox3","pcox1","coxboost","gacoxboost"),]
plotdt=plotdt[plotdt$variable=="hc"& plotdt$datasets==1,]
plotdt2=plotdt[!is.infinite(plotdt$value),]
plotdt2$model=factor(plotdt2$model,levels = c("pcox1","pcox3","coxboost","gacoxboost"))
ggplot(plotdt2, aes(x=model, y=value, fill=model)) + geom_boxplot()+theme_bw()#ggsave(plot=ggplot(plotdt2, aes(x=model, y=value, fill=model)) + geom_boxplot()+theme_bw(),file="figure2a1.pdf",device = "pdf")
plotdt=datafull[datafull$model%in% c("pcox3","pcox1","coxboost","gacoxboost"),]
plotdt=plotdt[plotdt$variable=="bs1"& plotdt$datasets==1,]
plotdt2=plotdt[!is.infinite(plotdt$value),]
plotdt2$model=factor(plotdt2$model,levels = c("pcox1","pcox3","coxboost","gacoxboost"))
ggplot(plotdt2, aes(x=model, y=value, fill=model)) + geom_boxplot()+theme_bw()#ggsave(plot=ggplot(plotdt2, aes(x=model, y=value, fill=model)) + geom_boxplot()+theme_bw(),file="figure2a2.pdf",device = "pdf")
plotdt=datafull[datafull$model%in% c("pcox3","pcox1","coxboost","gacoxboost"),]
plotdt=plotdt[plotdt$variable=="hc"& plotdt$datasets==16,]
plotdt2=plotdt[!is.infinite(plotdt$value),]
plotdt2$model=factor(plotdt2$model,levels = c("pcox1","pcox3","coxboost","gacoxboost"))
ggplot(plotdt2, aes(x=model, y=value, fill=model)) + geom_boxplot()+theme_bw()#ggsave(ggplot(plotdt2, aes(x=model, y=value, fill=model)) + geom_boxplot()+theme_bw(),file="figure2a3.pdf",device = "pdf")
plotdt=datafull[datafull$model%in% c("pcox3","pcox1","coxboost","gacoxboost"),]
plotdt=plotdt[plotdt$variable=="bs1"& plotdt$datasets==16,]
plotdt2=plotdt[!is.infinite(plotdt$value),]
plotdt2$model=factor(plotdt2$model,levels = c("pcox1","pcox3","coxboost","gacoxboost"))
ggplot(plotdt2, aes(x=model, y=value, fill=model)) + geom_boxplot()+theme_bw()plottdt=read_excel("result.xlsx",sheet = 8)
plottdt=plottdt[11,]
plottdt=as.data.frame(t(plottdt))
plottdt$data=rownames(plottdt)
plottdt=plottdt[which(plottdt$V1>0),]
plottdt=plottdt[-1,]
plottdt$V1=as.numeric(plottdt$V1)
#aes(x = "", y = V1, fill = data )
#ggsave(ggplot(data = plottdt,aes(x = reorder(data, V1), y = V1,fill = data )) + geom_bar(stat = "identity", position = position_dodge())+theme_bw(),file="figure1d2.pdf",device = "pdf")check linear model: which aspect affects cindex
regressiondt=datafull%>% filter(variable=="hc")%>% group_by(datasets,model)%>% dplyr::summarise(medianc=median(value))
regressiondt$datasets=mapvalues(regressiondt$datasets, from = seq(1,11,1), to = c("anz","us","veteran","lung","pbc","melanoma_clinical","melanoma_itraq","melanoma_swath","melanoma_nano","gse1","gse2"))
tablee=read_excel("result.xlsx",sheet = 9)
regressiondt1 <- tablee %>% left_join(regressiondt, by = c("name" = "datasets"))
regressiondt2<-regressiondt1%>% distinct()
fitted=lm(medianc~.,data = regressiondt2[,-1])
summary(fitted)##
## Call:
## lm(formula = medianc ~ ., data = regressiondt2[, -1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.307263 -0.052409 -0.000292 0.043616 0.190176
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.784e-01 4.134e-02 16.409 < 2e-16 ***
## num_obs -2.374e-06 1.195e-05 -0.199 0.842798
## num_feature 2.812e-06 1.358e-06 2.071 0.040095 *
## censoring_rate -7.277e-03 5.057e-02 -0.144 0.885769
## variable_typenc -5.051e-02 2.574e-02 -1.962 0.051595 .
## data_typeomics -1.149e-01 2.062e-02 -5.575 1.10e-07 ***
## modelcox2 5.610e-03 5.631e-02 0.100 0.920775
## modelcox3 3.211e-02 5.998e-02 0.535 0.593169
## modelcox4 -5.236e-02 5.631e-02 -0.930 0.353891
## modelcoxboost -1.255e-02 4.745e-02 -0.265 0.791700
## modeldeephit -1.024e-01 4.910e-02 -2.086 0.038605 *
## modeldeepsurv -9.319e-02 4.910e-02 -1.898 0.059569 .
## modeldnnsurv1 -1.064e-02 5.198e-02 -0.205 0.838057
## modelgacox -1.694e-02 4.839e-02 -0.350 0.726789
## modelgacoxboost -1.353e-02 4.839e-02 -0.280 0.780124
## modelgamtlr 1.766e-01 4.839e-02 3.650 0.000361 ***
## modellimmacoxboost -3.573e-02 4.817e-02 -0.742 0.459350
## modellimmamtlr 1.918e-01 4.745e-02 4.043 8.38e-05 ***
## modelmtlr1 1.679e-01 4.903e-02 3.424 0.000794 ***
## modelpcox1 -5.101e-03 4.745e-02 -0.108 0.914529
## modelpcox2 -1.543e-02 4.817e-02 -0.320 0.749122
## modelpcox3 8.742e-03 4.817e-02 0.181 0.856240
## modelrsf1 1.937e-02 4.745e-02 0.408 0.683601
## modelrsf2 1.611e-02 4.745e-02 0.339 0.734710
## modelsurvivalsvm -5.044e-02 5.068e-02 -0.995 0.321216
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.09254 on 152 degrees of freedom
## Multiple R-squared: 0.5352, Adjusted R-squared: 0.4618
## F-statistic: 7.293 on 24 and 152 DF, p-value: 1.694e-15
## # A tibble: 6 x 8
## name num_obs num_feature censoring_rate variable_type data_type model medianc
## <chr> <dbl> <dbl> <dbl> <chr> <chr> <chr> <dbl>
## 1 anz 3323 40 0.874 nc clinical cox1 0.620
## 2 anz 3323 40 0.874 nc clinical cox2 0.612
## 3 anz 3323 40 0.874 nc clinical cox3 0.620
## 4 anz 3323 40 0.874 nc clinical cox4 0.5
## 5 anz 3323 40 0.874 nc clinical coxb… 0.631
## 6 anz 3323 40 0.874 nc clinical deep… 0.555
##
## ===============================================
## Dependent variable:
## ---------------------------
## medianc
## -----------------------------------------------
## num_obs -0.00000
## (0.00001)
##
## num_feature 0.00000**
## (0.00000)
##
## censoring_rate -0.007
## (0.051)
##
## variable_typenc -0.051*
## (0.026)
##
## data_typeomics -0.115***
## (0.021)
##
## modelcox2 0.006
## (0.056)
##
## modelcox3 0.032
## (0.060)
##
## modelcox4 -0.052
## (0.056)
##
## modelcoxboost -0.013
## (0.047)
##
## modeldeephit -0.102**
## (0.049)
##
## modeldeepsurv -0.093*
## (0.049)
##
## modeldnnsurv1 -0.011
## (0.052)
##
## modelgacox -0.017
## (0.048)
##
## modelgacoxboost -0.014
## (0.048)
##
## modelgamtlr 0.177***
## (0.048)
##
## modellimmacoxboost -0.036
## (0.048)
##
## modellimmamtlr 0.192***
## (0.047)
##
## modelmtlr1 0.168***
## (0.049)
##
## modelpcox1 -0.005
## (0.047)
##
## modelpcox2 -0.015
## (0.048)
##
## modelpcox3 0.009
## (0.048)
##
## modelrsf1 0.019
## (0.047)
##
## modelrsf2 0.016
## (0.047)
##
## modelsurvivalsvm -0.050
## (0.051)
##
## Constant 0.678***
## (0.041)
##
## -----------------------------------------------
## Observations 177
## R2 0.535
## Adjusted R2 0.462
## Residual Std. Error 0.093 (df = 152)
## F Statistic 7.293*** (df = 24; 152)
## ===============================================
## Note: *p<0.1; **p<0.05; ***p<0.01
more added on 20210429, check linear model: which aspect affects cindex
regressiondt=datafull%>% filter(variable=="hc")%>% group_by(datasets,model)%>% dplyr::summarise(medianc=median(value))
regressiondt$datasets=mapvalues(regressiondt$datasets, from = seq(1,16,1), to = c("anz","us","veteran","lung","pbc","melanoma_clinical","melanoma_itraq","melanoma_nano","gse1","gse2","ngene1","ngene2","ngene3","ngene4","ngene5","ngene6"))
regressiondt=regressiondt[regressiondt$model=="rsf1",]
tablee=read_excel("result.xlsx",sheet = 10)
regressiondt1 <- tablee %>% left_join(regressiondt, by = c("Data" = "datasets"))
regressiondt2=regressiondt1[,c(3:9,13)]
fitted=lm(medianc~.,data = regressiondt2)
summary(fitted)
head(regressiondt2)
stargazer(fitted, type = "text")heatmap of cindex for model vs data
olddata_long=regressiondt2[,c(1,7,8)]
data_wide <- tidyr::spread(olddata_long,model, medianc)
data_wide
plotdt=as.data.frame(data_wide)
rownames(plotdt)=plotdt$name
#pheatmap(as.matrix(plotdt[,-1]),cluster_rows = FALSE,cluster_cols = TRUE)
col_fun = colorRamp2(c( 0.4,1), c("seagreen", "lightpink"))
mat=as.matrix(plotdt[,-1])
mat[is.na(mat)] <- 0
Heatmap(mat, name = "index", col = col_fun,cluster_rows = FALSE,cluster_columns = FALSE,rect_gp = gpar(col = "white", lwd = 2))check for this data pbc from another package: still, mtlr is bad
data("pbc",package = "survival")
dim(pbc)
colnames(pbc)
pbc1 <- pbc[,-1]
dim(pbc1)
any(is.na(pbc1))
pbc2=na.omit(pbc1)
dim(pbc2) #276 19
table(pbc2$status)
pbc3=pbc2[pbc2$status!=1,]
pbc3$status=as.vector(ifelse(pbc3$status==2,1,0))
table(pbc3$status) #147 111
fitform_ogl=as.formula(Surv(time, status) ~ .)
summary(pbc3$time)
formula1=fitform_ogl
formula2=fitform_ogl
formula3=Surv(time,status)~1
formula4=Surv(time,status)~1
timess=seq(50,2000,(2000-50)/15)
#9
cox1 <- pbmcapply::pbmclapply(1:20, rsf2_fun,pbc3,5, fitform_ogl,formula1,formula2,formula3,formula4,timess, mc.cores = 2)
head(cox1)
colMeans(do.call(rbind, cox1))
#10
cox2 <- pbmcapply::pbmclapply(1:20, mtlr_fun,pbc3,5, fitform_ogl,formula1,formula2,formula3,formula4,timess, mc.cores = 2)
head(cox2)
colMeans(do.call(rbind, cox2))time_fun=function(model_list){
time_vec=c()
for (i in 1:20){
dt=model_list[[i]]
if (is.null(dt)){time_vec[i]=NA} else {time_vec[i]=dt}
}
return(time_vec)
}
memory_fun=function(model_list){
memory_vec=c()
for (i in 1:20){
dt=model_list[[i]]
if (is.null(dt)){memory_vec[i]=NA} else {memory_vec[i]=sum(dt$by.total$mem.total)}
}
return(memory_vec)
}
plot_fun1=function(valuee){
df=data.frame(methods=c("cox1","cox2","cox3","cox4","pcox1","pcox2","pcox3","rsf1","rsf2","mtlr1","dnnsurv1","coxboost","gacox","gamtlr","gacoxboost","limmamtlr","limmacoxboost","survivalsvm","deepsurv","deephit"), value=valuee)
p<-ggplot(data=df, aes(x=methods, y=value)) +
geom_bar(stat="identity")+ggtitle(" memory(MB)")+theme_bw()
return(p)
}
plot_fun2=function(valuee){
df=data.frame(methods=c("cox1","cox2","cox3","cox4","pcox1","pcox2","pcox3","rsf1","rsf2","mtlr1","dnnsurv1","coxboost","gacox","gamtlr","gacoxboost","limmamtlr","limmacoxboost","survivalsvm","deepsurv","deephit"), value=valuee)
p<-ggplot(data=df, aes(x=methods, y=value)) +
geom_bar(stat="identity")+ggtitle(" time(seconds)")+theme_bw()
return(p)
}#pbc
cox1=readRDS("savedresults/pbc_cox1m.rds")
cox2=readRDS("savedresults/pbc_bw_cox1m.rds")
cox3=readRDS("savedresults/pbc_bw_cox2m.rds")
cox4=readRDS("savedresults/pbc_bw_cox3m.rds")
pcox1=readRDS("savedresults/pbc_p_cox1m.rds")
pcox2=readRDS("savedresults/pbc_p_cox2m.rds")
pcox3=readRDS("savedresults/pbc_p_cox3m.rds")
rsf1=readRDS("savedresults/pbc_rsf1m.rds")
rsf2=readRDS("savedresults/pbc_rsf2m.rds")
mtlr1=readRDS("savedresults/pbc_mtlrm.rds")
dnnsurv1=readRDS("savedresults/pbc_dnnsurvm.rds")
coxboost=readRDS("savedresults/pbc_coxboostm.rds")
survivalsvm=readRDS("savedresults/pbc_survivalsvmm.rds")
ga_cox=readRDS("savedresults/pbc_ga_cox1m.rds")
ga_mtlr=readRDS("savedresults/pbc_ga_mtlrm.rds")
ga_coxboost=readRDS("savedresults/pbc_ga_coxboostm.rds")
limma_mtlr=readRDS("savedresults/pbc_limma_mtlrm.rds")
limma_coxboost=readRDS("savedresults/pbc_limma_coxboostm.rds")
deepsurv=readRDS("savedresults/pbc_deepsurvm.rds")
deephit=readRDS("savedresults/pbc_deephitm.rds")
model_list1=list(cox1,cox2,cox3,cox4,pcox1,pcox2,pcox3,rsf1,rsf2,mtlr1,dnnsurv1,coxboost,ga_cox,ga_mtlr,ga_coxboost,limma_mtlr,limma_coxboost,survivalsvm,deepsurv,deephit)
tcox1=readRDS("savedresults/pbc_cox1t.rds")
tcox2=readRDS("savedresults/pbc_bw_cox1t.rds")
tcox3=readRDS("savedresults/pbc_bw_cox2t.rds")
tcox4=readRDS("savedresults/pbc_bw_cox3t.rds")
tpcox1=readRDS("savedresults/pbc_p_cox1t.rds")
tpcox2=readRDS("savedresults/pbc_p_cox2t.rds")
tpcox3=readRDS("savedresults/pbc_p_cox3t.rds")
trsf1=readRDS("savedresults/pbc_rsf1t.rds")
trsf2=readRDS("savedresults/pbc_rsf2t.rds")
tmtlr1=readRDS("savedresults/pbc_mtlrt.rds")
tdnnsurv1=readRDS("savedresults/pbc_dnnsurvt.rds")*60
tcoxboost=readRDS("savedresults/pbc_coxboostt.rds")
tsurvivalsvm=readRDS("savedresults/pbc_survivalsvmt.rds")*60
tga_cox=readRDS("savedresults/pbc_ga_cox1t.rds")
tga_mtlr=readRDS("savedresults/pbc_ga_mtlrt.rds")
tga_coxboost=readRDS("savedresults/pbc_ga_coxboostt.rds")
tlimma_mtlr=readRDS("savedresults/pbc_limma_mtlrt.rds")
tlimma_coxboost=readRDS("savedresults/pbc_limma_coxboostt.rds")
tdeepsurv=readRDS("savedresults/pbc_deepsurvt.rds")*60
tdeephit=readRDS("savedresults/pbc_deephitt.rds")*60
model_list2=list(tcox1,tcox2,tcox3,tcox4,tpcox1,tpcox2,tpcox3,trsf1,trsf2,tmtlr1,tdnnsurv1,tcoxboost,tga_cox,tga_mtlr,tga_coxboost,tlimma_mtlr,tlimma_coxboost,tsurvivalsvm,tdeepsurv,tdeephit)
m=memory_fun(model_list1)
t=time_fun(model_list2)
m1=as.vector(ifelse(m>10,"large",ifelse(m<5,"small","medium")))
t1=as.vector(ifelse(t<100,"fast",ifelse(t>1000,"slow","ok")))
g1=plot_fun1(m)
g2=plot_fun2(t)
pbcall=cbind.data.frame(m,t,m1,t1)
pbcall$methods=c("cox1","cox2","cox3","cox4","pcox1","pcox2","pcox3","rsf1","rsf2","mtlr1","dnnsurv1","coxboost","gacox","gamtlr","gacoxboost","limmamtlr","limmacoxboost","survivalsvm","deepsurv","deephit")
#veteran
cox1=readRDS("savedresults/veteran_cox1m.rds")
cox2=readRDS("savedresults/veteran_bw_cox1m.rds")
cox3=readRDS("savedresults/veteran_bw_cox2m.rds")
cox4=readRDS("savedresults/veteran_bw_cox3m.rds")
pcox1=readRDS("savedresults/veteran_p_cox1m.rds")
pcox2=readRDS("savedresults/veteran_p_cox2m.rds")
pcox3=readRDS("savedresults/veteran_p_cox3m.rds")
rsf1=readRDS("savedresults/veteran_rsf1m.rds")
rsf2=readRDS("savedresults/veteran_rsf2m.rds")
mtlr1=readRDS("savedresults/veteran_mtlrm.rds")
dnnsurv1=readRDS("savedresults/veteran_dnnsurvm.rds")
coxboost=readRDS("savedresults/veteran_coxboostm.rds")
survivalsvm=readRDS("savedresults/veteran_survivalsvmm.rds")
ga_cox=readRDS("savedresults/veteran_ga_cox1m.rds")
ga_mtlr=readRDS("savedresults/veteran_ga_mtlrm.rds")
ga_coxboost=readRDS("savedresults/veteran_ga_coxboostm.rds")
limma_mtlr=readRDS("savedresults/veteran_limma_mtlrm.rds")
limma_coxboost=readRDS("savedresults/veteran_limma_coxboostm.rds")
deepsurv=readRDS("savedresults/pbc_deepsurvm.rds")
deephit=readRDS("savedresults/pbc_deephitm.rds")
model_list1=list(cox1,cox2,cox3,cox4,pcox1,pcox2,pcox3,rsf1,rsf2,mtlr1,dnnsurv1,coxboost,ga_cox,ga_mtlr,ga_coxboost,limma_mtlr,limma_coxboost,survivalsvm,deepsurv,deephit)
tcox1=readRDS("savedresults/veteran_cox1t.rds")
tcox2=readRDS("savedresults/veteran_bw_cox1t.rds")
tcox3=readRDS("savedresults/veteran_bw_cox2t.rds")
tcox4=readRDS("savedresults/veteran_bw_cox3t.rds")
tpcox1=readRDS("savedresults/veteran_p_cox1t.rds")
tpcox2=readRDS("savedresults/veteran_p_cox2t.rds")
tpcox3=readRDS("savedresults/veteran_p_cox3t.rds")
trsf1=readRDS("savedresults/veteran_rsf1t.rds")
trsf2=readRDS("savedresults/veteran_rsf2t.rds")
tmtlr1=readRDS("savedresults/veteran_mtlrt.rds")
tdnnsurv1=readRDS("savedresults/veteran_dnnsurvt.rds")*60
tcoxboost=readRDS("savedresults/veteran_coxboostt.rds")
tsurvivalsvm=readRDS("savedresults/veteran_survivalsvmt.rds")
tga_cox=readRDS("savedresults/veteran_ga_cox1t.rds")
tga_mtlr=readRDS("savedresults/veteran_ga_mtlrt.rds")
tga_coxboost=readRDS("savedresults/veteran_ga_coxboostt.rds")
tlimma_mtlr=readRDS("savedresults/veteran_limma_mtlrt.rds")
tlimma_coxboost=readRDS("savedresults/veteran_limma_coxboostt.rds")
tdeepsurv=readRDS("savedresults/veteran_deepsurvt.rds")*60
tdeephit=readRDS("savedresults/veteran_deephitt.rds")*60
model_list2=list(tcox1,tcox2,tcox3,tcox4,tpcox1,tpcox2,tpcox3,trsf1,trsf2,tmtlr1,tdnnsurv1,tcoxboost,tga_cox,tga_mtlr,tga_coxboost,tlimma_mtlr,tlimma_coxboost,tsurvivalsvm,tdeepsurv,tdeephit)
m=memory_fun(model_list1)
t=time_fun(model_list2)
m1=as.vector(ifelse(m>10,"large",ifelse(m<5,"small","medium")))
t1=as.vector(ifelse(t<100,"fast",ifelse(t>1000,"slow","ok")))
g3=plot_fun1(m)
g4=plot_fun2(t)
veteranall=cbind.data.frame(m,t,m1,t1)
veteranall$methods=c("cox1","cox2","cox3","cox4","pcox1","pcox2","pcox3","rsf1","rsf2","mtlr1","dnnsurv1","coxboost","gacox","gamtlr","gacoxboost","limmamtlr","limmacoxboost","survivalsvm","deepsurv","deephit")
#lung
cox1=readRDS("savedresults/lung_cox1m.rds")
cox2=readRDS("savedresults/lung_bw_cox1m.rds")
cox3=readRDS("savedresults/lung_bw_cox2m.rds")
pcox1=readRDS("savedresults/lung_p_cox1m.rds")
pcox2=readRDS("savedresults/lung_p_cox2m.rds")
pcox3=readRDS("savedresults/lung_p_cox3m.rds")
rsf1=readRDS("savedresults/lung_rsf1m.rds")
rsf2=readRDS("savedresults/lung_rsf2m.rds")
mtlr1=readRDS("savedresults/lung_mtlrm.rds")
dnnsurv1=readRDS("savedresults/lung_dnnsurvm.rds")
coxboost=readRDS("savedresults/lung_coxboostm.rds")
cox4=readRDS("savedresults/lung_bw_cox3m.rds")
survivalsvm=readRDS("savedresults/lung_survivalsvmm.rds")
ga_cox=readRDS("savedresults/lung_ga_cox1m.rds")
ga_mtlr=readRDS("savedresults/lung_ga_mtlrm.rds")
ga_coxboost=readRDS("savedresults/lung_ga_coxboostm.rds")
limma_mtlr=readRDS("savedresults/lung_limma_mtlrm.rds")
limma_coxboost=readRDS("savedresults/lung_limma_coxboostm.rds")
deepsurv=readRDS("savedresults/lung_deepsurvm.rds")
deephit=readRDS("savedresults/lung_deephitm.rds")
model_list1=list(cox1,cox2,cox3,cox4,pcox1,pcox2,pcox3,rsf1,rsf2,mtlr1,dnnsurv1,coxboost,ga_cox,ga_mtlr,ga_coxboost,limma_mtlr,limma_coxboost,survivalsvm,deepsurv,deephit)
tcox1=readRDS("savedresults/lung_cox1t.rds")
tcox2=readRDS("savedresults/lung_bw_cox1t.rds")
tcox3=readRDS("savedresults/lung_bw_cox2t.rds")
tpcox1=readRDS("savedresults/lung_p_cox1t.rds")
tpcox2=readRDS("savedresults/lung_p_cox2t.rds")
tpcox3=readRDS("savedresults/lung_p_cox3t.rds")
trsf1=readRDS("savedresults/lung_rsf1t.rds")
trsf2=readRDS("savedresults/lung_rsf2t.rds")
tmtlr1=readRDS("savedresults/lung_mtlrt.rds")
tdnnsurv1=readRDS("savedresults/lung_dnnsurvt.rds")*60
tcoxboost=readRDS("savedresults/lung_coxboostt.rds")
tcox4=readRDS("savedresults/lung_bw_cox3t.rds")
tsurvivalsvm=readRDS("savedresults/lung_survivalsvmt.rds")
tga_cox=readRDS("savedresults/lung_ga_cox1t.rds")
tga_mtlr=readRDS("savedresults/lung_ga_mtlrt.rds")
tga_coxboost=readRDS("savedresults/lung_ga_coxboostt.rds")
tlimma_mtlr=readRDS("savedresults/lung_limma_mtlrt.rds")
tlimma_coxboost=readRDS("savedresults/lung_limma_coxboostt.rds")
tdeepsurv=readRDS("savedresults/lung_deepsurvt.rds")*60
tdeephit=readRDS("savedresults/lung_deephitt.rds")*60
model_list2=list(tcox1,tcox2,tcox3,tcox4,tpcox1,tpcox2,tpcox3,trsf1,trsf2,tmtlr1,tdnnsurv1,tcoxboost,tga_cox,tga_mtlr,tga_coxboost,tlimma_mtlr,tlimma_coxboost,tsurvivalsvm,tdeepsurv,tdeephit)
m=memory_fun(model_list1)
t=time_fun(model_list2)
m1=as.vector(ifelse(m>10,"large",ifelse(m<5,"small","medium")))
t1=as.vector(ifelse(t<100,"fast",ifelse(t>1000,"slow","ok")))
g5=plot_fun1(m)
g6=plot_fun2(t)
lungall=cbind.data.frame(m,t,m1,t1)
lungall$methods=c("cox1","cox2","cox3","cox4","pcox1","pcox2","pcox3","rsf1","rsf2","mtlr1","dnnsurv1","coxboost","gacox","gamtlr","gacoxboost","limmamtlr","limmacoxboost","survivalsvm","deepsurv","deephit")
#anz
cox1=readRDS("savedresults/anz_cox1m.rds")
cox2=readRDS("savedresults/anz_bw_cox1m.rds")
cox3=readRDS("savedresults/anz_bw_cox2m.rds")
cox4=readRDS("savedresults/anz_bw_cox3m.rds")
pcox1=readRDS("savedresults/anz_p_cox1m.rds")
pcox2=readRDS("savedresults/anz_p_cox2m.rds")
pcox3=readRDS("savedresults/anz_p_cox3m.rds")
rsf1=readRDS("savedresults/anz_rsf1m.rds")
rsf2=readRDS("savedresults/anz_rsf2m.rds")
mtlr1=readRDS("savedresults/anz_mtlrm.rds")
dnnsurv1=readRDS("savedresults/anz_dnnsurvm.rds")
coxboost=readRDS("savedresults/anz_coxboostm.rds")
survivalsvm=NULL
ga_cox=readRDS("savedresults/anz_ga_cox1m.rds")
ga_mtlr=readRDS("savedresults/anz_ga_mtlrm.rds")
ga_coxboost=readRDS("savedresults/anz_ga_coxboostm.rds")
limma_mtlr=readRDS("savedresults/anz_limma_mtlrm.rds")
limma_coxboost=readRDS("savedresults/anz_limma_coxboostm.rds")
deepsurv=readRDS("savedresults/anz_deepsurvm.rds")
deephit=readRDS("savedresults/anz_deephitm.rds")
model_list1=list(cox1,cox2,cox3,cox4,pcox1,pcox2,pcox3,rsf1,rsf2,mtlr1,dnnsurv1,coxboost,ga_cox,ga_mtlr,ga_coxboost,limma_mtlr,limma_coxboost,survivalsvm,deepsurv,deephit)
tcox1=readRDS("savedresults/anz_cox1t.rds")
tcox2=readRDS("savedresults/anz_bw_cox1t.rds")
tcox3=readRDS("savedresults/anz_bw_cox2t.rds")
tcox4=readRDS("savedresults/anz_bw_cox3t.rds")
tpcox1=readRDS("savedresults/anz_p_cox1t.rds")*60
tpcox2=readRDS("savedresults/anz_p_cox2t.rds")
tpcox3=readRDS("savedresults/anz_p_cox3t.rds")*60
trsf1=readRDS("savedresults/anz_rsf1t.rds")*60
trsf2=readRDS("savedresults/anz_rsf2t.rds")*60
tmtlr1=readRDS("savedresults/anz_mtlrt.rds")*60
tdnnsurv1=readRDS("savedresults/anz_dnnsurvt.rds")*60
tcoxboost=readRDS("savedresults/anz_coxboostt.rds")
tsurvivalsvm=NULL
tga_cox=readRDS("savedresults/anz_ga_cox1t.rds")
tga_mtlr=readRDS("savedresults/anz_ga_mtlrt.rds")
tga_coxboost=readRDS("savedresults/anz_ga_coxboostt.rds")
tlimma_mtlr=readRDS("savedresults/anz_limma_mtlrt.rds")
tlimma_coxboost=readRDS("savedresults/anz_limma_coxboostt.rds")
tdeepsurv=readRDS("savedresults/anz_deepsurvt.rds")*60
tdeephit=readRDS("savedresults/anz_deephitt.rds")*60
model_list2=list(tcox1,tcox2,tcox3,tcox4,tpcox1,tpcox2,tpcox3,trsf1,trsf2,tmtlr1,tdnnsurv1,tcoxboost,tga_cox,tga_mtlr,tga_coxboost,tlimma_mtlr,tlimma_coxboost,tsurvivalsvm,tdeepsurv,tdeephit)
m=memory_fun(model_list1)
t=time_fun(model_list2)
m1=as.vector(ifelse(m>10,"large",ifelse(m<5,"small","medium")))
t1=as.vector(ifelse(t<100,"fast",ifelse(t>1000,"slow","ok")))
g7=plot_fun1(m)
g8=plot_fun2(t)
anzall=cbind.data.frame(m,t,m1,t1)
anzall$methods=c("cox1","cox2","cox3","cox4","pcox1","pcox2","pcox3","rsf1","rsf2","mtlr1","dnnsurv1","coxboost","gacox","gamtlr","gacoxboost","limmamtlr","limmacoxboost","survivalsvm","deepsurv","deephit")
#us
cox1=readRDS("savedresults/us_cox1m.rds")
cox2=NULL
cox3=NULL
cox4=NULL
pcox1=readRDS("savedresults/us_p_cox1m.rds")
pcox2=readRDS("savedresults/us_p_cox2m.rds")
pcox3=readRDS("savedresults/us_p_cox3m.rds")
rsf1=readRDS("savedresults/us_rsf1m.rds")
rsf2=readRDS("savedresults/us_rsf2m.rds")
mtlr1=readRDS("savedresults/us_mtlrm.rds")
dnnsurv1=NULL
coxboost=readRDS("savedresults/us_coxboostm.rds")
survivalsvm=NULL
ga_cox=NULL
ga_mtlr=NULL
ga_coxboost=NULL
limma_mtlr=readRDS("savedresults/us_limma_mtlrm.rds")
limma_coxboost=readRDS("savedresults/us_limma_coxboostm.rds")
deepsurv=readRDS("savedresults/us_deepsurvm.rds")
deephit=readRDS("savedresults/us_deephitm.rds")
model_list1=list(cox1,cox2,cox3,cox4,pcox1,pcox2,pcox3,rsf1,rsf2,mtlr1,dnnsurv1,coxboost,ga_cox,ga_mtlr,ga_coxboost,limma_mtlr,limma_coxboost,survivalsvm,deepsurv,deephit)
tcox1=readRDS("savedresults/us_cox1t.rds")
tcox2=NULL
tcox3=NULL
tcox4=NULL
tpcox1=readRDS("savedresults/us_p_cox1t.rds")*3600
tpcox2=readRDS("savedresults/us_p_cox2t.rds")
tpcox3=readRDS("savedresults/us_p_cox3t.rds")*3600
trsf1=readRDS("savedresults/us_rsf1t.rds")*3600
trsf2=readRDS("savedresults/us_rsf2t.rds")*60
tmtlr1=readRDS("savedresults/us_mtlrt.rds")*3600
tdnnsurv1=NULL
tcoxboost=readRDS("savedresults/us_coxboostt.rds")
tsurvivalsvm=NULL
tga_cox=NULL
tga_mtlr=NULL
tga_coxboost=NULL
tlimma_mtlr=readRDS("savedresults/us_limma_mtlrt.rds")*60
tlimma_coxboost=readRDS("savedresults/us_limma_coxboostt.rds")
tdeepsurv=readRDS("savedresults/us_deepsurvt.rds")*3600
tdeephit=readRDS("savedresults/us_deephitt.rds")*3600
model_list2=list(tcox1,tcox2,tcox3,tcox4,tpcox1,tpcox2,tpcox3,trsf1,trsf2,tmtlr1,tdnnsurv1,tcoxboost,tga_cox,tga_mtlr,tga_coxboost,tlimma_mtlr,tlimma_coxboost,tsurvivalsvm,tdeepsurv,tdeephit)
m=memory_fun(model_list1)
t=time_fun(model_list2)
m1=as.vector(ifelse(m>10,"large",ifelse(m<5,"small","medium")))
t1=as.vector(ifelse(t<100,"fast",ifelse(t>1000,"slow","ok")))
g9=plot_fun1(m)
g10=plot_fun2(t)
usall=cbind.data.frame(m,t,m1,t1)
usall$methods=c("cox1","cox2","cox3","cox4","pcox1","pcox2","pcox3","rsf1","rsf2","mtlr1","dnnsurv1","coxboost","gacox","gamtlr","gacoxboost","limmamtlr","limmacoxboost","survivalsvm","deepsurv","deephit")
#melanomaclinical
cox1=readRDS("savedresults/melanomaclinical_cox1m.rds")
cox2=NULL
cox3=NULL
cox4=NULL
pcox1=readRDS("savedresults/melanomaclinical_p_cox1m.rds")
pcox2=readRDS("savedresults/melanomaclinical_p_cox2m.rds")
pcox3=readRDS("savedresults/melanomaclinical_p_cox3m.rds")
rsf1=readRDS("savedresults/melanomaclinical_rsf1m.rds")
rsf2=readRDS("savedresults/melanomaclinical_rsf2m.rds")
mtlr1=readRDS("savedresults/melanomaclinical_mtlrm.rds")
dnnsurv1=readRDS("savedresults/melanomaclinical_dnnsurvm.rds")
coxboost=readRDS("savedresults/melanomaclinica_coxboostm.rds")
survivalsvm=NULL
ga_cox=NULL
ga_mtlr=NULL
ga_coxboost=NULL
limma_mtlr=readRDS("savedresults/melanomaclinical_limma_mtlrm.rds")
limma_coxboost=readRDS("savedresults/melanomaclinical_limma_coxboostm.rds")
deepsurv=readRDS("savedresults/melanomaclinical_deepsurvm.rds")
deephit=readRDS("savedresults/melanomaclinical_deephitm.rds")
model_list1=list(cox1,cox2,cox3,cox4,pcox1,pcox2,pcox3,rsf1,rsf2,mtlr1,dnnsurv1,coxboost,ga_cox,ga_mtlr,ga_coxboost,limma_mtlr,limma_coxboost,survivalsvm,deepsurv,deephit)
tcox1=readRDS("savedresults/melanomaclinical_cox1t.rds")
tcox2=NULL
tcox3=NULL
tcox4=NULL
tpcox1=readRDS("savedresults/melanomaclinical_p_cox1t.rds")
tpcox2=readRDS("savedresults/melanomaclinical_p_cox2t.rds")
tpcox3=readRDS("savedresults/melanomaclinical_p_cox3t.rds")
trsf1=readRDS("savedresults/melanomaclinical_rsf1t.rds")*60
trsf2=readRDS("savedresults/melanomaclinical_rsf2t.rds")
tmtlr1=readRDS("savedresults/melanomaclinical_mtlrt.rds")
tdnnsurv1=readRDS("savedresults/melanomaclinical_dnnsurvt.rds")*60
tcoxboost=readRDS("savedresults/melanomaclinica_coxboostt.rds")
tsurvivalsvm=NULL
tga_cox=NULL
tga_mtlr=NULL
tga_coxboost=NULL
tlimma_mtlr=readRDS("savedresults/melanomaclinical_limma_mtlrt.rds")*60
tlimma_coxboost=readRDS("savedresults/melanomaclinical_limma_coxboostt.rds")
tdeepsurv=readRDS("savedresults/melanomaclinical_deepsurvt.rds")*60
tdeephit=readRDS("savedresults/melanomaclinical_deephitt.rds")*60
model_list2=list(tcox1,tcox2,tcox3,tcox4,tpcox1,tpcox2,tpcox3,trsf1,trsf2,tmtlr1,tdnnsurv1,tcoxboost,tga_cox,tga_mtlr,tga_coxboost,tlimma_mtlr,tlimma_coxboost,tsurvivalsvm,tdeepsurv,tdeephit)
m=memory_fun(model_list1)
t=time_fun(model_list2)
m1=as.vector(ifelse(m>10,"large",ifelse(m<5,"small","medium")))
t1=as.vector(ifelse(t<100,"fast",ifelse(t>1000,"slow","ok")))
g11=plot_fun1(m)
g12=plot_fun2(t)
melanomaclinicalall=cbind.data.frame(m,t,m1,t1)
melanomaclinicalall$methods=c("cox1","cox2","cox3","cox4","pcox1","pcox2","pcox3","rsf1","rsf2","mtlr1","dnnsurv1","coxboost","gacox","gamtlr","gacoxboost","limmamtlr","limmacoxboost","survivalsvm","deepsurv","deephit")
#melanomaitraq
cox1=NULL
cox2=NULL
cox3=NULL
cox4=NULL
pcox1=readRDS("savedresults/melanomaitraqv2_p_cox1m.rds")
pcox2=NULL
pcox3=NULL
rsf1=readRDS("savedresults/melanomaitraqv2_rsf1m.rds")
rsf2=readRDS("savedresults/melanomaitraqv2_rsf2m.rds")
mtlr1=readRDS("savedresults/melanomaitraqv2_mtlrm.rds")
dnnsurv1=readRDS("savedresults/melanomaitraqv2_dnnsurvm.rds")
coxboost=readRDS("savedresults/melanomaitraqv2_coxboostm.rds")
ga_cox=readRDS("savedresults/itraqv2_ga_cox1m.rds")
ga_mtlr=readRDS("savedresults/melanomaitraqv2_ga_mtlrm.rds")
ga_coxboost=readRDS("savedresults/melanomaitraqv2_ga_coxboostm.rds")
limma_mtlr=readRDS("savedresults/melanomaitraqv2_limma_mtlrm.rds")
limma_coxboost=readRDS("savedresults/melanomaitraqv2_limma_coxboostm.rds")
survivalsvm=readRDS("savedresults/itraqv2_survivalsvmm.rds")
deepsurv=readRDS("savedresults/melanomaitraqv2_deepsurvm.rds")
deephit=readRDS("savedresults/melanomaitraqv2_deephitm.rds")
model_list1=list(cox1,cox2,cox3,cox4,pcox1,pcox2,pcox3,rsf1,rsf2,mtlr1,dnnsurv1,coxboost,ga_cox,ga_mtlr,ga_coxboost,limma_mtlr,limma_coxboost,survivalsvm,deepsurv,deephit)
tcox1=NULL
tcox2=NULL
tcox3=NULL
tcox4=NULL
tpcox1=readRDS("savedresults/melanomaitraq_p_cox1t.rds")
tpcox2=NULL
tpcox3=NULL
trsf1=readRDS("savedresults/melanomaitraqv2_rsf1t.rds")
trsf2=readRDS("savedresults/melanomaitraqv2_rsf2t.rds")
tmtlr1=readRDS("savedresults/melanomaitraqv2_mtlrt.rds")*60
tdnnsurv1=readRDS("savedresults/melanomaitraqv2_dnnsurvt.rds")
tcoxboost=readRDS("savedresults/melanomaitraqv2_coxboostt.rds")
tga_cox=readRDS("savedresults/itraqv2_ga_cox1t.rds")
tga_mtlr=readRDS("savedresults/melanomaitraqv2_ga_mtlrt.rds")
tga_coxboost=readRDS("savedresults/melanomaitraqv2_ga_coxboostt.rds")
tlimma_mtlr=readRDS("savedresults/melanomaitraqv2_limma_mtlrt.rds")
tlimma_coxboost=readRDS("savedresults/melanomaitraqv2_limma_coxboostt.rds")
tsurvivalsvm=readRDS("savedresults/itraqv2_survivalsvmt.rds")
tdeepsurv=readRDS("savedresults/melanomaitraqv2_deepsurvt.rds")
tdeephit=readRDS("savedresults/melanomaitraqv2_deephitt.rds")
model_list2=list(tcox1,tcox2,tcox3,tcox4,tpcox1,tpcox2,tpcox3,trsf1,trsf2,tmtlr1,tdnnsurv1,tcoxboost,tga_cox,tga_mtlr,tga_coxboost,tlimma_mtlr,tlimma_coxboost,tsurvivalsvm,tdeepsurv,tdeephit)
m=memory_fun(model_list1)
t=time_fun(model_list2)
m1=as.vector(ifelse(m>10,"large",ifelse(m<5,"small","medium")))
t1=as.vector(ifelse(t<100,"fast",ifelse(t>1000,"slow","ok")))
g13=plot_fun1(m)
g14=plot_fun2(t)
melanomaitraqall=cbind.data.frame(m,t,m1,t1)
melanomaitraqall$methods=c("cox1","cox2","cox3","cox4","pcox1","pcox2","pcox3","rsf1","rsf2","mtlr1","dnnsurv1","coxboost","gacox","gamtlr","gacoxboost","limmamtlr","limmacoxboost","survivalsvm","deepsurv","deephit")
#melanomanano
cox1=NULL
cox2=readRDS("savedresults/melanomananov3_bw_cox1m.rds")
cox3=readRDS("savedresults/melanomananov3_bw_cox2m.rds")
cox4=readRDS("savedresults/melanomananov3_bw_cox3m.rds")
pcox1=readRDS("savedresults/melanomananov3_p_cox1m.rds")
pcox2=readRDS("savedresults/melanomananov3_p_cox2m.rds")
pcox3=readRDS("savedresults/melanomananov3_p_cox3m.rds")
rsf1=readRDS("savedresults/melanomanano_rsf1m.rds")
rsf2=readRDS("savedresults/melanomanano_rsf2m.rds")
mtlr1=readRDS("savedresults/melanomananov3_mtlrm.rds")
dnnsurv1=readRDS("savedresults/melanomananov3_dnnsurvm.rds")
coxboost=readRDS("savedresults/melanomananov3_coxboostm.rds")
ga_cox=readRDS("savedresults/melanomananov3_ga_cox1m.rds")
ga_mtlr=readRDS("savedresults/melanomananov3_ga_mtlrm.rds")
ga_coxboost=readRDS("savedresults/melanomananov3_ga_coxboostm.rds")
limma_mtlr=readRDS("savedresults/melanomananov3_limma_mtlrm.rds")
limma_coxboost=readRDS("savedresults/melanomananov3_limma_coxboostm.rds")
survivalsvm=NULL
deepsurv=readRDS("savedresults/melanomananov3_deepsurvm.rds")
deephit=readRDS("savedresults/melanomananov3_deephitm.rds")
model_list1=list(cox1,cox2,cox3,cox4,pcox1,pcox2,pcox3,rsf1,rsf2,mtlr1,dnnsurv1,coxboost,ga_cox,ga_mtlr,ga_coxboost,limma_mtlr,limma_coxboost,survivalsvm,deepsurv,deephit)
tcox1=NULL
tcox2=readRDS("savedresults/melanomananov3_bw_cox1t.rds")
tcox3=readRDS("savedresults/melanomananov3_bw_cox2t.rds")
tcox4=readRDS("savedresults/melanomananov3_bw_cox3t.rds")
tpcox1=readRDS("savedresults/melanomananov3_p_cox1t.rds")
tpcox2=readRDS("savedresults/melanomananov3_p_cox2t.rds")
tpcox3=readRDS("savedresults/melanomananov3_p_cox3t.rds")
trsf1=readRDS("savedresults/melanomanano_rsf1t.rds")
trsf2=readRDS("savedresults/melanomanano_rsf2t.rds")
tmtlr1=readRDS("savedresults/melanomananov3_mtlrt.rds")
tdnnsurv1=readRDS("savedresults/melanomananov3_dnnsurvt.rds")
tcoxboost=readRDS("savedresults/melanomananov3_coxboostt.rds")
tga_cox=readRDS("savedresults/melanomananov3_ga_cox1t.rds")
tga_mtlr=readRDS("savedresults/melanomananov3_ga_mtlrt.rds")
tga_coxboost=readRDS("savedresults/melanomananov3_ga_coxboostt.rds")
tlimma_mtlr=readRDS("savedresults/melanomananov3_limma_mtlrt.rds")
tlimma_coxboost=readRDS("savedresults/melanomananov3_limma_coxboostt.rds")
tsurvivalsvm=NULL
tdeepsurv=readRDS("savedresults/melanomananov3_deepsurvt.rds")
tdeephit=readRDS("savedresults/melanomananov3_deephitt.rds")
model_list2=list(tcox1,tcox2,tcox3,tcox4,tpcox1,tpcox2,tpcox3,trsf1,trsf2,tmtlr1,tdnnsurv1,tcoxboost,tga_cox,tga_mtlr,tga_coxboost,tlimma_mtlr,tlimma_coxboost,tsurvivalsvm,tdeepsurv,tdeephit)
m=memory_fun(model_list1)
t=time_fun(model_list2)
m1=as.vector(ifelse(m>10,"large",ifelse(m<5,"small","medium")))
t1=as.vector(ifelse(t<100,"fast",ifelse(t>1000,"slow","ok")))
g17=plot_fun1(m)
g18=plot_fun2(t)
melanomananoall=cbind.data.frame(m,t,m1,t1)
melanomananoall$methods=c("cox1","cox2","cox3","cox4","pcox1","pcox2","pcox3","rsf1","rsf2","mtlr1","dnnsurv1","coxboost","gacox","gamtlr","gacoxboost","limmamtlr","limmacoxboost","survivalsvm","deepsurv","deephit")
#gse1
cox1=NULL
cox2=NULL
cox3=NULL
cox4=NULL
pcox1=readRDS("savedresults/gse1_p_cox1m.rds")
pcox2=readRDS("savedresults/gse1_p_cox2m.rds")
pcox3=readRDS("savedresults/gse1_p_cox3m.rds")
rsf1=readRDS("savedresults/gse1_rsf1m.rds")
rsf2=readRDS("savedresults/gse1_rsf2m.rds")
mtlr1=NULL
dnnsurv1=NULL
coxboost=readRDS("savedresults/gse1_coxboostm.rds")
ga_cox=readRDS("savedresults/gse1_ga_cox1m.rds")
ga_mtlr=readRDS("savedresults/gse1_ga_mtlrm.rds")
ga_coxboost=readRDS("savedresults/gse1_ga_coxboostm.rds")
limma_mtlr=readRDS("savedresults/gse1_limma_mtlrm.rds")
limma_coxboost=readRDS("savedresults/gse1_limma_coxboostm.rds")
survivalsvm=readRDS("savedresults/gse1_survivalsvmm.rds")
deepsurv=readRDS("savedresults/gse1_deepsurvm.rds")
deephit=readRDS("savedresults/gse1_deephitm.rds")
model_list1=list(cox1,cox2,cox3,cox4,pcox1,pcox2,pcox3,rsf1,rsf2,mtlr1,dnnsurv1,coxboost,ga_cox,ga_mtlr,ga_coxboost,limma_mtlr,limma_coxboost,survivalsvm,deepsurv,deephit)
tcox1=NULL
tcox2=NULL
tcox3=NULL
tcox4=NULL
tpcox1=readRDS("savedresults/gse1_p_cox1t.rds")*60
tpcox2=readRDS("savedresults/gse1_p_cox2t.rds")*60
tpcox3=readRDS("savedresults/gse1_p_cox3t.rds")*60
trsf1=readRDS("savedresults/gse1_rsf1t.rds")*60
trsf2=readRDS("savedresults/gse1_rsf2t.rds")*60
tmtlr1=NULL
tdnnsurv1=NULL
tcoxboost=readRDS("savedresults/gse1_coxboostt.rds")*60
tga_cox=readRDS("savedresults/gse1_ga_cox1t.rds")*60
tga_mtlr=readRDS("savedresults/gse1_ga_mtlrt.rds")*60
tga_coxboost=readRDS("savedresults/gse1_ga_coxboostt.rds")*60
tlimma_mtlr=readRDS("savedresults/gse1_limma_mtlrt.rds")*60
tlimma_coxboost=readRDS("savedresults/gse1_limma_coxboostt.rds")
tsurvivalsvm=readRDS("savedresults/gse1_survivalsvmt.rds")
tdeepsurv=readRDS("savedresults/melanomananov3_deepsurvt.rds")*60
tdeephit=readRDS("savedresults/melanomananov3_deephitt.rds")*60
model_list2=list(tcox1,tcox2,tcox3,tcox4,tpcox1,tpcox2,tpcox3,trsf1,trsf2,tmtlr1,tdnnsurv1,tcoxboost,tga_cox,tga_mtlr,tga_coxboost,tlimma_mtlr,tlimma_coxboost,tsurvivalsvm,tdeepsurv,tdeephit)
m=memory_fun(model_list1)
t=time_fun(model_list2)
m1=as.vector(ifelse(m>10,"large",ifelse(m<5,"small","medium")))
t1=as.vector(ifelse(t<100,"fast",ifelse(t>1000,"slow","ok")))
g19=plot_fun1(m)
g20=plot_fun2(t)
gse1all=cbind.data.frame(m,t,m1,t1)
gse1all$methods=c("cox1","cox2","cox3","cox4","pcox1","pcox2","pcox3","rsf1","rsf2","mtlr1","dnnsurv1","coxboost","gacox","gamtlr","gacoxboost","limmamtlr","limmacoxboost","survivalsvm","deepsurv","deephit")
#gse2
cox1=NULL
cox2=NULL
cox3=NULL
cox4=NULL
pcox1=readRDS("savedresults/gse4_p_cox1m.rds")
pcox2=readRDS("savedresults/gse4_p_cox2m.rds")
pcox3=readRDS("savedresults/gse4_p_cox3m.rds")
rsf1=readRDS("savedresults/gse4_rsf1m.rds")
rsf2=readRDS("savedresults/gse4_rsf2m.rds")
mtlr1=NULL
dnnsurv1=NULL
coxboost=readRDS("savedresults/gse4_coxboostm.rds")
ga_cox=readRDS("savedresults/gse4_ga_cox1m.rds")
ga_mtlr=readRDS("savedresults/gse4_ga_mtlrm.rds")
ga_coxboost=readRDS("savedresults/gse4_ga_coxboostm.rds")
limma_mtlr=readRDS("savedresults/gse4_limma_mtlrm.rds")
limma_coxboost=readRDS("savedresults/gse4_limma_coxboostm.rds")
survivalsvm=readRDS("savedresults/gse4_survivalsvmm.rds")
deepsurv=NULL
deephit=NULL
model_list1=list(cox1,cox2,cox3,cox4,pcox1,pcox2,pcox3,rsf1,rsf2,mtlr1,dnnsurv1,coxboost,ga_cox,ga_mtlr,ga_coxboost,limma_mtlr,limma_coxboost,survivalsvm,deepsurv,deephit)
tcox1=NULL
tcox2=NULL
tcox3=NULL
tcox4=NULL
tpcox1=readRDS("savedresults/gse4_p_cox1t.rds")*60
tpcox2=readRDS("savedresults/gse4_p_cox2t.rds")*60
tpcox3=readRDS("savedresults/gse4_p_cox3t.rds")*60
trsf1=readRDS("savedresults/gse4_rsf1t.rds")*60
trsf2=readRDS("savedresults/gse4_rsf2t.rds")*60
tmtlr1=NULL
tdnnsurv1=NULL
tcoxboost=readRDS("savedresults/gse4_coxboostt.rds")
tga_cox=readRDS("savedresults/gse4_ga_cox1t.rds")*60
tga_mtlr=readRDS("savedresults/gse4_ga_mtlrt.rds")*60
tga_coxboost=readRDS("savedresults/gse4_ga_coxboostt.rds")*60
tlimma_mtlr=readRDS("savedresults/gse4_limma_mtlrt.rds")*60
tlimma_coxboost=readRDS("savedresults/gse4_limma_coxboostt.rds")
tsurvivalsvm=readRDS("savedresults/gse4_survivalsvmt.rds")
tdeepsurv=NULL
tdeephit=NULL
model_list2=list(tcox1,tcox2,tcox3,tcox4,tpcox1,tpcox2,tpcox3,trsf1,trsf2,tmtlr1,tdnnsurv1,tcoxboost,tga_cox,tga_mtlr,tga_coxboost,tlimma_mtlr,tlimma_coxboost,tsurvivalsvm,tdeepsurv,tdeephit)
t=time_fun(model_list2)
m1=as.vector(ifelse(m>10,"large",ifelse(m<5,"small","medium")))
t1=as.vector(ifelse(t<100,"fast",ifelse(t>1000,"slow","ok")))
g21=plot_fun1(m)
g22=plot_fun2(t)
gse2all=cbind.data.frame(m,t,m1,t1)
gse2all$methods=c("cox1","cox2","cox3","cox4","pcox1","pcox2","pcox3","rsf1","rsf2","mtlr1","dnnsurv1","coxboost","gacox","gamtlr","gacoxboost","limmamtlr","limmacoxboost","survivalsvm","deepsurv","deephit")
#ngene1
cox1=NULL
cox2=NULL
cox3=NULL
cox4=NULL
pcox1=readRDS("savedresults/ngse1_p_cox1m.rds")
pcox2=readRDS("savedresults/ngse1_p_cox2m.rds")
pcox3=readRDS("savedresults/ngse1_p_cox3m.rds")
rsf1=readRDS("savedresults/ngse1_rsf1m.rds")
rsf2=readRDS("savedresults/ngse1_rsf2m.rds")
mtlr1=readRDS("savedresults/ngse1_mtlrm.rds")
dnnsurv1=readRDS("savedresults/ngse1_dnnsurvm.rds")
coxboost=readRDS("savedresults/ngse1_coxboostm.rds")
ga_cox=readRDS("savedresults/ngse1_ga_cox1m.rds")
ga_mtlr=readRDS("savedresults/ngse1_ga_mtlrm.rds")
ga_coxboost=readRDS("savedresults/ngse1_ga_coxboostm.rds")
limma_mtlr=readRDS("savedresults/ngse1_limma_mtlrm.rds")
limma_coxboost=readRDS("savedresults/ngse1_limma_coxboostm.rds")
survivalsvm=readRDS("savedresults/ngse1_survivalsvmm.rds")
deepsurv=NULL
deephit=NULL
model_list1=list(cox1,cox2,cox3,cox4,pcox1,pcox2,pcox3,rsf1,rsf2,mtlr1,dnnsurv1,coxboost,ga_cox,ga_mtlr,ga_coxboost,limma_mtlr,limma_coxboost,survivalsvm,deepsurv,deephit)
tcox1=NULL
tcox2=NULL
tcox3=NULL
tcox4=NULL
tpcox1=readRDS("savedresults/ngse1_p_cox1t.rds")
tpcox2=readRDS("savedresults/ngse1_p_cox2t.rds")
tpcox3=readRDS("savedresults/ngse1_p_cox3t.rds")
trsf1=readRDS("savedresults/ngse1_rsf1t.rds")
trsf2=readRDS("savedresults/ngse1_rsf2t.rds")
tmtlr1=readRDS("savedresults/ngse1_mtlrt.rds")*60
tdnnsurv1=readRDS("savedresults/ngse1_dnnsurvt.rds")*60
tcoxboost=readRDS("savedresults/ngse1_coxboostt.rds")
tga_cox=readRDS("savedresults/ngse1_ga_cox1t.rds")*60
tga_mtlr=readRDS("savedresults/ngse1_ga_mtlrt.rds")*60
tga_coxboost=readRDS("savedresults/ngse1_ga_coxboostt.rds")*60
tlimma_mtlr=readRDS("savedresults/ngse1_limma_mtlrt.rds")
tlimma_coxboost=readRDS("savedresults/ngse1_limma_coxboostt.rds")
tsurvivalsvm=readRDS("savedresults/ngse1_survivalsvmt.rds")
tdeepsurv=NULL
tdeephit=NULL
model_list2=list(tcox1,tcox2,tcox3,tcox4,tpcox1,tpcox2,tpcox3,trsf1,trsf2,tmtlr1,tdnnsurv1,tcoxboost,tga_cox,tga_mtlr,tga_coxboost,tlimma_mtlr,tlimma_coxboost,tsurvivalsvm,tdeepsurv,tdeephit)
m=memory_fun(model_list1)
t=time_fun(model_list2)
m1=as.vector(ifelse(m>10,"large",ifelse(m<5,"small","medium")))
t1=as.vector(ifelse(t<100,"fast",ifelse(t>1000,"slow","ok")))
g23=plot_fun1(m)
g24=plot_fun2(t)
ngene1all=cbind.data.frame(m,t,m1,t1)
ngene1all$methods=c("cox1","cox2","cox3","cox4","pcox1","pcox2","pcox3","rsf1","rsf2","mtlr1","dnnsurv1","coxboost","gacox","gamtlr","gacoxboost","limmamtlr","limmacoxboost","survivalsvm","deepsurv","deephit")
#ngene2
cox1=NULL
cox2=NULL
cox3=NULL
cox4=NULL
pcox1=readRDS("savedresults/ngse2_p_cox1m.rds")
pcox2=readRDS("savedresults/ngse2_p_cox2m.rds")
pcox3=readRDS("savedresults/ngse2_p_cox3m.rds")
rsf1=readRDS("savedresults/ngse2_rsf1m.rds")
rsf2=readRDS("savedresults/ngse2_rsf2m.rds")
mtlr1=readRDS("savedresults/ngse2_mtlrm.rds")
dnnsurv1=readRDS("savedresults/ngse2_dnnsurvm.rds")
coxboost=readRDS("savedresults/ngse2_coxboostm.rds")
ga_cox=readRDS("savedresults/ngse2_ga_cox1m.rds")
ga_mtlr=readRDS("savedresults/ngse2_ga_mtlrm.rds")
ga_coxboost=readRDS("savedresults/ngse2_ga_coxboostm.rds")
limma_mtlr=readRDS("savedresults/ngse2_limma_mtlrm.rds")
limma_coxboost=readRDS("savedresults/ngse2_limma_coxboostm.rds")
survivalsvm=readRDS("savedresults/ngse2_survivalsvmm.rds")
deepsurv=readRDS("savedresults/ngse2_deepsurvm.rds")
deephit=readRDS("savedresults/ngse2_deephitm.rds")
model_list1=list(cox1,cox2,cox3,cox4,pcox1,pcox2,pcox3,rsf1,rsf2,mtlr1,dnnsurv1,coxboost,ga_cox,ga_mtlr,ga_coxboost,limma_mtlr,limma_coxboost,survivalsvm,deepsurv,deephit)
tcox1=NULL
tcox2=NULL
tcox3=NULL
tcox4=NULL
tpcox1=readRDS("savedresults/ngse2_p_cox1t.rds")*60
tpcox2=readRDS("savedresults/ngse2_p_cox2t.rds")*60
tpcox3=readRDS("savedresults/ngse2_p_cox3t.rds")*60
trsf1=readRDS("savedresults/ngse2_rsf1t.rds")*60
trsf2=readRDS("savedresults/ngse2_rsf2t.rds")*60
tmtlr1=readRDS("savedresults/ngse2_mtlrt.rds")*3600
tdnnsurv1=readRDS("savedresults/ngse2_dnnsurvt.rds")*60
tcoxboost=readRDS("savedresults/ngse2_coxboostt.rds")
tga_cox=readRDS("savedresults/ngse2_ga_cox1t.rds")*60
tga_mtlr=readRDS("savedresults/ngse2_ga_mtlrt.rds")*60
tga_coxboost=readRDS("savedresults/ngse2_ga_coxboostt.rds")*60
tlimma_mtlr=readRDS("savedresults/ngse2_limma_mtlrt.rds")
tlimma_coxboost=readRDS("savedresults/ngse2_limma_coxboostt.rds")
tsurvivalsvm=readRDS("savedresults/ngse2_survivalsvmt.rds")*60
tdeepsurv=readRDS("savedresults/ngse2_deepsurvt.rds")*60
tdeephit=readRDS("savedresults/ngse2_deephitt.rds")*60
model_list2=list(tcox1,tcox2,tcox3,tcox4,tpcox1,tpcox2,tpcox3,trsf1,trsf2,tmtlr1,tdnnsurv1,tcoxboost,tga_cox,tga_mtlr,tga_coxboost,tlimma_mtlr,tlimma_coxboost,tsurvivalsvm,tdeepsurv,tdeephit)
m=memory_fun(model_list1)
t=time_fun(model_list2)
m1=as.vector(ifelse(m>10,"large",ifelse(m<5,"small","medium")))
t1=as.vector(ifelse(t<100,"fast",ifelse(t>1000,"slow","ok")))
g25=plot_fun1(m)
g26=plot_fun2(t)
ngene2all=cbind.data.frame(m,t,m1,t1)
ngene2all$methods=c("cox1","cox2","cox3","cox4","pcox1","pcox2","pcox3","rsf1","rsf2","mtlr1","dnnsurv1","coxboost","gacox","gamtlr","gacoxboost","limmamtlr","limmacoxboost","survivalsvm","deepsurv","deephit")
#ngene3
cox1=NULL
cox2=NULL
cox3=NULL
cox4=NULL
pcox1=readRDS("savedresults/ngse3_p_cox1m.rds")
pcox2=readRDS("savedresults/ngse3_p_cox2m.rds")
pcox3=readRDS("savedresults/ngse3_p_cox3m.rds")
rsf1=readRDS("savedresults/ngse3_rsf1m.rds")
rsf2=readRDS("savedresults/ngse3_rsf2m.rds")
mtlr1=readRDS("savedresults/ngse3_mtlrm.rds")
dnnsurv1=readRDS("savedresults/ngse3_dnnsurvm.rds")
coxboost=readRDS("savedresults/ngse3_coxboostm.rds")
ga_cox=readRDS("savedresults/ngse3_ga_cox1m.rds")
ga_mtlr=readRDS("savedresults/ngse3_ga_mtlrm.rds")
ga_coxboost=readRDS("savedresults/ngse3_ga_coxboostm.rds")
limma_mtlr=readRDS("savedresults/ngse3_limma_mtlrm.rds")
limma_coxboost=readRDS("savedresults/ngse3_limma_coxboostm.rds")
survivalsvm=NULL
deepsurv=readRDS("savedresults/ngse3_deepsurvm.rds")
deephit=readRDS("savedresults/ngse3_deephitm.rds")
model_list1=list(cox1,cox2,cox3,cox4,pcox1,pcox2,pcox3,rsf1,rsf2,mtlr1,dnnsurv1,coxboost,ga_cox,ga_mtlr,ga_coxboost,limma_mtlr,limma_coxboost,survivalsvm,deepsurv,deephit)
tcox1=NULL
tcox2=NULL
tcox3=NULL
tcox4=NULL
tpcox1=readRDS("savedresults/ngse3_p_cox1t.rds")
tpcox2=readRDS("savedresults/ngse3_p_cox2t.rds")*60
tpcox3=readRDS("savedresults/ngse3_p_cox3t.rds")
trsf1=readRDS("savedresults/ngse3_rsf1t.rds")*60
trsf2=readRDS("savedresults/ngse3_rsf2t.rds")*60
tmtlr1=readRDS("savedresults/ngse3_mtlrt.rds")*3600
tdnnsurv1=readRDS("savedresults/ngse3_dnnsurvt.rds")*60
tcoxboost=readRDS("savedresults/ngse3_coxboostt.rds")
tga_cox=readRDS("savedresults/ngse3_ga_cox1t.rds")*60
tga_mtlr=readRDS("savedresults/ngse3_ga_mtlrt.rds")*60
tga_coxboost=readRDS("savedresults/ngse3_ga_coxboostt.rds")*60
tlimma_mtlr=readRDS("savedresults/ngse3_limma_mtlrt.rds")
tlimma_coxboost=readRDS("savedresults/ngse3_limma_coxboostt.rds")
tsurvivalsvm=NULL
tdeepsurv=readRDS("savedresults/ngse3_deepsurvt.rds")*60
tdeephit=readRDS("savedresults/ngse3_deephitt.rds")*60
model_list2=list(tcox1,tcox2,tcox3,tcox4,tpcox1,tpcox2,tpcox3,trsf1,trsf2,tmtlr1,tdnnsurv1,tcoxboost,tga_cox,tga_mtlr,tga_coxboost,tlimma_mtlr,tlimma_coxboost,tsurvivalsvm,tdeepsurv,tdeephit)
m=memory_fun(model_list1)
t=time_fun(model_list2)
m1=as.vector(ifelse(m>10,"large",ifelse(m<5,"small","medium")))
t1=as.vector(ifelse(t<100,"fast",ifelse(t>1000,"slow","ok")))
g27=plot_fun1(m)
g28=plot_fun2(t)
ngene3all=cbind.data.frame(m,t,m1,t1)
ngene3all$methods=c("cox1","cox2","cox3","cox4","pcox1","pcox2","pcox3","rsf1","rsf2","mtlr1","dnnsurv1","coxboost","gacox","gamtlr","gacoxboost","limmamtlr","limmacoxboost","survivalsvm","deepsurv","deephit")
#ngene4
cox1=NULL
cox2=NULL
cox3=NULL
cox4=NULL
pcox1=readRDS("savedresults/ngse4_p_cox1m.rds")
pcox2=readRDS("savedresults/ngse4_p_cox2m.rds")
pcox3=readRDS("savedresults/ngse4_p_cox3m.rds")
rsf1=readRDS("savedresults/ngse4_rsf1m.rds")
rsf2=readRDS("savedresults/ngse4_rsf2m.rds")
mtlr1=readRDS("savedresults/ngse4_mtlrm.rds")
dnnsurv1=readRDS("savedresults/ngse4_dnnsurvm.rds")
coxboost=readRDS("savedresults/ngse4_coxboostm.rds")
ga_cox=readRDS("savedresults/ngse4_ga_cox1m.rds")
ga_mtlr=readRDS("savedresults/ngse4_ga_mtlrm.rds")
ga_coxboost=readRDS("savedresults/ngse4_ga_coxboostm.rds")
limma_mtlr=readRDS("savedresults/ngse4_limma_mtlrm.rds")
limma_coxboost=readRDS("savedresults/ngse4_limma_coxboostm.rds")
survivalsvm=readRDS("savedresults/ngse4_survivalsvmm.rds")
deepsurv=readRDS("savedresults/ngse4_deepsurvm.rds")
deephit=readRDS("savedresults/ngse4_deephitm.rds")
model_list1=list(cox1,cox2,cox3,cox4,pcox1,pcox2,pcox3,rsf1,rsf2,mtlr1,dnnsurv1,coxboost,ga_cox,ga_mtlr,ga_coxboost,limma_mtlr,limma_coxboost,survivalsvm,deepsurv,deephit)
tcox1=NULL
tcox2=NULL
tcox3=NULL
tcox4=NULL
tpcox1=readRDS("savedresults/ngse4_p_cox1t.rds")
tpcox2=readRDS("savedresults/ngse4_p_cox2t.rds")
tpcox3=readRDS("savedresults/ngse4_p_cox3t.rds")
trsf1=readRDS("savedresults/ngse4_rsf1t.rds")*60
trsf2=readRDS("savedresults/ngse4_rsf2t.rds")*60
tmtlr1=readRDS("savedresults/ngse4_mtlrt.rds")*3600
tdnnsurv1=readRDS("savedresults/ngse4_dnnsurvt.rds")*60
tcoxboost=readRDS("savedresults/ngse4_coxboostt.rds")
tga_cox=readRDS("savedresults/ngse4_ga_cox1t.rds")*60
tga_mtlr=readRDS("savedresults/ngse4_ga_mtlrt.rds")*60
tga_coxboost=readRDS("savedresults/ngse4_ga_coxboostt.rds")*60
tlimma_mtlr=readRDS("savedresults/ngse4_limma_mtlrt.rds")
tlimma_coxboost=readRDS("savedresults/ngse4_limma_coxboostt.rds")
tsurvivalsvm=readRDS("savedresults/ngse4_survivalsvmt.rds")
tdeepsurv=readRDS("savedresults/ngse4_deepsurvt.rds")*60
tdeephit=readRDS("savedresults/ngse4_deephitt.rds")*60
model_list2=list(tcox1,tcox2,tcox3,tcox4,tpcox1,tpcox2,tpcox3,trsf1,trsf2,tmtlr1,tdnnsurv1,tcoxboost,tga_cox,tga_mtlr,tga_coxboost,tlimma_mtlr,tlimma_coxboost,tsurvivalsvm,tdeepsurv,tdeephit)
m=memory_fun(model_list1)
t=time_fun(model_list2)
m1=as.vector(ifelse(m>10,"large",ifelse(m<5,"small","medium")))
t1=as.vector(ifelse(t<100,"fast",ifelse(t>1000,"slow","ok")))
g29=plot_fun1(m)
g30=plot_fun2(t)
ngene4all=cbind.data.frame(m,t,m1,t1)
ngene4all$methods=c("cox1","cox2","cox3","cox4","pcox1","pcox2","pcox3","rsf1","rsf2","mtlr1","dnnsurv1","coxboost","gacox","gamtlr","gacoxboost","limmamtlr","limmacoxboost","survivalsvm","deepsurv","deephit")
#ngene5
cox1=NULL
cox2=NULL
cox3=NULL
cox4=NULL
pcox1=readRDS("savedresults/ngse5_p_cox1m.rds")
pcox2=readRDS("savedresults/ngse5_p_cox2m.rds")
pcox3=readRDS("savedresults/ngse5_p_cox3m.rds")
rsf1=readRDS("savedresults/ngse5_rsf1m.rds")
rsf2=readRDS("savedresults/ngse5_rsf2m.rds")
mtlr1=readRDS("savedresults/ngse5_mtlrm.rds")
dnnsurv1=readRDS("savedresults/ngse5_dnnsurvm.rds")
coxboost=readRDS("savedresults/ngse5_coxboostm.rds")
ga_cox=readRDS("savedresults/ngse5_ga_cox1m.rds")
ga_mtlr=readRDS("savedresults/ngse5_ga_mtlrm.rds")
ga_coxboost=readRDS("savedresults/ngse5_ga_coxboostm.rds")
limma_mtlr=readRDS("savedresults/ngse5_limma_mtlrm.rds")
limma_coxboost=readRDS("savedresults/ngse5_limma_coxboostm.rds")
survivalsvm=readRDS("savedresults/ngse5_survivalsvmm.rds")
deepsurv=readRDS("savedresults/ngse5_deepsurvm.rds")
deephit=readRDS("savedresults/ngse5_deephitm.rds")
model_list1=list(cox1,cox2,cox3,cox4,pcox1,pcox2,pcox3,rsf1,rsf2,mtlr1,dnnsurv1,coxboost,ga_cox,ga_mtlr,ga_coxboost,limma_mtlr,limma_coxboost,survivalsvm,deepsurv,deephit)
tcox1=NULL
tcox2=NULL
tcox3=NULL
tcox4=NULL
tpcox1=readRDS("savedresults/ngse5_p_cox1t.rds")
tpcox2=readRDS("savedresults/ngse5_p_cox2t.rds")
tpcox3=readRDS("savedresults/ngse5_p_cox3t.rds")
trsf1=readRDS("savedresults/ngse5_rsf1t.rds")*60
trsf2=readRDS("savedresults/ngse5_rsf2t.rds")*60
tmtlr1=readRDS("savedresults/ngse5_mtlrt.rds")*60
tdnnsurv1=readRDS("savedresults/ngse5_dnnsurvt.rds")*60
tcoxboost=readRDS("savedresults/ngse5_coxboostt.rds")
tga_cox=readRDS("savedresults/ngse5_ga_cox1t.rds")*60
tga_mtlr=readRDS("savedresults/ngse5_ga_mtlrt.rds")*60
tga_coxboost=readRDS("savedresults/ngse5_ga_coxboostt.rds")*60
tlimma_mtlr=readRDS("savedresults/ngse5_limma_mtlrt.rds")
tlimma_coxboost=readRDS("savedresults/ngse5_limma_coxboostt.rds")
tsurvivalsvm=readRDS("savedresults/ngse5_survivalsvmt.rds")
tdeepsurv=readRDS("savedresults/ngse5_deepsurvt.rds")*60
tdeephit=readRDS("savedresults/ngse5_deephitt.rds")*60
model_list2=list(tcox1,tcox2,tcox3,tcox4,tpcox1,tpcox2,tpcox3,trsf1,trsf2,tmtlr1,tdnnsurv1,tcoxboost,tga_cox,tga_mtlr,tga_coxboost,tlimma_mtlr,tlimma_coxboost,tsurvivalsvm,tdeepsurv,tdeephit)
m=memory_fun(model_list1)
t=time_fun(model_list2)
m1=as.vector(ifelse(m>10,"large",ifelse(m<5,"small","medium")))
t1=as.vector(ifelse(t<100,"fast",ifelse(t>1000,"slow","ok")))
g31=plot_fun1(m)
g32=plot_fun2(t)
ngene5all=cbind.data.frame(m,t,m1,t1)
ngene5all$methods=c("cox1","cox2","cox3","cox4","pcox1","pcox2","pcox3","rsf1","rsf2","mtlr1","dnnsurv1","coxboost","gacox","gamtlr","gacoxboost","limmamtlr","limmacoxboost","survivalsvm","deepsurv","deephit")
#ngene6
cox1=NULL
cox2=NULL
cox3=NULL
cox4=NULL
pcox1=readRDS("savedresults/ngse6_p_cox1m.rds")
pcox2=readRDS("savedresults/ngse6_p_cox2m.rds")
pcox3=readRDS("savedresults/ngse6_p_cox3m.rds")
rsf1=readRDS("savedresults/ngse6_rsf1m.rds")
rsf2=readRDS("savedresults/ngse6_rsf2m.rds")
mtlr1=readRDS("savedresults/ngse6_mtlrm.rds")
dnnsurv1=readRDS("savedresults/ngse6_dnnsurvm.rds")
coxboost=readRDS("savedresults/ngse6_coxboostm.rds")
ga_cox=readRDS("savedresults/ngse6_ga_cox1m.rds")
ga_mtlr=readRDS("savedresults/ngse6_ga_mtlrm.rds")
ga_coxboost=readRDS("savedresults/ngse6_ga_coxboostm.rds")
limma_mtlr=readRDS("savedresults/ngse6_limma_mtlrm.rds")
limma_coxboost=readRDS("savedresults/ngse6_limma_coxboostm.rds")
survivalsvm=readRDS("savedresults/ngse6_survivalsvmm.rds")
deepsurv=readRDS("savedresults/ngse6_deepsurvm.rds")
deephit=readRDS("savedresults/ngse6_deephitm.rds")
model_list1=list(cox1,cox2,cox3,cox4,pcox1,pcox2,pcox3,rsf1,rsf2,mtlr1,dnnsurv1,coxboost,ga_cox,ga_mtlr,ga_coxboost,limma_mtlr,limma_coxboost,survivalsvm,deepsurv,deephit)
tcox1=NULL
tcox2=NULL
tcox3=NULL
tcox4=NULL
tpcox1=readRDS("savedresults/ngse6_p_cox1t.rds")*60
tpcox2=readRDS("savedresults/ngse6_p_cox2t.rds")*60
tpcox3=readRDS("savedresults/ngse6_p_cox3t.rds")*60
trsf1=readRDS("savedresults/ngse6_rsf1t.rds")*60
trsf2=readRDS("savedresults/ngse6_rsf2t.rds")*60
tmtlr1=readRDS("savedresults/ngse6_mtlrt.rds")*3600
tdnnsurv1=readRDS("savedresults/ngse6_dnnsurvt.rds")*60
tcoxboost=readRDS("savedresults/ngse6_coxboostt.rds")
tga_cox=readRDS("savedresults/ngse6_ga_cox1t.rds")*60
tga_mtlr=readRDS("savedresults/ngse6_ga_mtlrt.rds")*60
tga_coxboost=readRDS("savedresults/ngse6_ga_coxboostt.rds")
tlimma_mtlr=readRDS("savedresults/ngse6_limma_mtlrt.rds")
tlimma_coxboost=readRDS("savedresults/ngse6_limma_coxboostt.rds")
tsurvivalsvm=readRDS("savedresults/ngse6_survivalsvmt.rds")*60
tdeepsurv=readRDS("savedresults/ngse6_deepsurvt.rds")*60
tdeephit=readRDS("savedresults/ngse6_deephitt.rds")*60
model_list2=list(tcox1,tcox2,tcox3,tcox4,tpcox1,tpcox2,tpcox3,trsf1,trsf2,tmtlr1,tdnnsurv1,tcoxboost,tga_cox,tga_mtlr,tga_coxboost,tlimma_mtlr,tlimma_coxboost,tsurvivalsvm,tdeepsurv,tdeephit)
m=memory_fun(model_list1)
t=time_fun(model_list2)
m1=as.vector(ifelse(m>10,"large",ifelse(m<5,"small","medium")))
t1=as.vector(ifelse(t<100,"fast",ifelse(t>1000,"slow","ok")))
g33=plot_fun1(m)
g34=plot_fun2(t)
ngene6all=cbind.data.frame(m,t,m1,t1)
ngene6all$methods=c("cox1","cox2","cox3","cox4","pcox1","pcox2","pcox3","rsf1","rsf2","mtlr1","dnnsurv1","coxboost","gacox","gamtlr","gacoxboost","limmamtlr","limmacoxboost","survivalsvm","deepsurv","deephit")labelss<-c("pbc","veteran","lung","anz","us","melanoma_clinical","melanoma_itraq","melanoma_swath","melanoma_nano","gse1","gse2","ngene1","ngene2","ngene3","ngene4","ngene5","ngene6")
ggarrange(g1,g3,g5,g7,g9,g11,g13,g15,g17,g19,g21,g23,g25,g27,g29,g31,g33,labels = labelss,ncol = 3, nrow = 6)
ggarrange(g2,g4,g6,g8,g10,g12,g14,g16,g18,g20,g22,g24,g24,g26,g28,g30,g32,g34,labels = labelss,ncol = 3, nrow = 6)library(DT)
data_list2=list(pbcall,veteranall,lungall,anzall,usall,melanomaclinicalall,melanomaitraqall,melanomananoall,gse1all,gse2all,ngene1all,ngene2all,ngene3all,ngene4all,ngene5all,ngene6all)
datafull2=bind_rows(data_list2, .id = "datasets")
datafull2$datasets=mapvalues(datafull2$datasets, from = seq(1,16,1), to = c("pbc","veteran","lung","anz","us","melanomaclinical","melanomaitraq","melanomanano","gse1","gse2","ngene1","ngene2","mgene3","ngene4","ngene5","ngene6"))
datafull2$m2=ifelse(datafull2$m>3,"large",ifelse(datafull2$m<1,"small","medium"))
datafull2$t2=ifelse(datafull2$t<50,"fast",ifelse(datafull2$t>1000,"slow","ok"))
DT::datatable(datafull2)## datasets m t m1 t1 methods m2 t2
## 68 anz 6.6 1803.785 medium slow rsf1 large slow
## 71 anz 22.7 1236.030 large slow dnnsurv1 large slow
## 85 us 20.4 21670.952 large slow pcox1 large slow
## 87 us 7.6 8868.669 medium slow pcox3 large slow
## 88 us 4.8 4113.810 small slow rsf1 large slow
## 90 us 8.6 9217.211 medium slow mtlr1 large slow
## 99 us 18.0 3917.326 large slow deepsurv large slow
## 108 melanomaclinical 5.2 2032.390 medium slow rsf1 large slow
## 168 gse1 4.9 2956.533 small slow rsf1 large slow
## 176 gse1 3393.8 1025.182 large slow limmamtlr large slow
## 188 gse2 4.9 1932.757 small slow rsf1 large slow
## 230 ngene2 3.5 9644.277 small slow mtlr1 large slow
## 270 ngene4 3.1 3922.781 small slow mtlr1 large slow
## 290 ngene5 4.0 2270.606 small slow mtlr1 large slow
data_list2=list(pbcall,veteranall,lungall,anzall,usall,melanomaclinicalall,melanomaitraqall,melanomananoall,gse1all,gse2all,ngene1all,ngene2all,ngene3all,ngene4all,ngene5all,ngene6all)
datafull2=bind_rows(data_list2, .id = "datasets")
datafull2$datasets=mapvalues(datafull2$datasets, from = seq(1,16,1), to = c("pbc","veteran","lung","anz","us","melanomaclinical","melanomaitraq","melanomanano","gse1","gse2","ngene1","ngene2","mgene3","ngene4","ngene5","ngene6"))
timememorydt=datafull2%>% group_by(methods) %>%dplyr::summarize(Mean_time = mean(t, na.rm=TRUE),Mean_memory=mean(m,na.rm = TRUE))
datafull3=datafull
datafull3$datasets=mapvalues(datafull3$datasets, from = seq(1,16,1), to = c("anz","us","veteran","lung","pbc","melanoma_clinical","melanomaitraq","melanomanano","gse1","gse2","ngene1","ngene2","mgene3","ngene4","ngene5","ngene6"))
predictiondt=datafull3%>% group_by(model,variable) %>%dplyr::summarize(Mean_value = mean(value, na.rm=TRUE), SD_value=sd(value,na.rm = TRUE))
predictiondt2=predictiondt[predictiondt$variable %in% c("hc", "unoc", "bs1","auc1", "auc5", "auc10", "auc15"),]
predictiondt3 <- tidyr::spread(predictiondt2[,1:3], variable, Mean_value)
predictiondt4=tidyr::spread(predictiondt2[,c(1,2,4)], variable, SD_value)
allsummarydt=cbind.data.frame(timememorydt,predictiondt3[,2:8],predictiondt4[,2:8])
colnames(allsummarydt)=c("methods","Mean_time","Mean_memory","Mean_hc","Mean_unoc","Mean_bs","Mean_auc1","Mean_auc5","Mean_auc10","Mean_auc15","SD_hc","SD_unoc","SD_bs","SD_auc1","SD_auc5","SD_auc10","SD_auc15")
#allsummarydt[allsummarydt$methods=="survivalsvm","Mean_bs"]==10*5#set to a large number because bs cant be calculated for survivalsvm actually
allsummarydt2=select(allsummarydt, -methods) %>% mutate_all(funs(dense_rank(desc(.))))
allsummarydt3=select(allsummarydt, -methods) %>% mutate_all(funs(rank(.)))
allsummarydt4=cbind.data.frame(allsummarydt2[,c(3,4,6,7,8,9)],allsummarydt3[,c(1,2,5,10:16)])
rownames(allsummarydt4)=allsummarydt$methods
nb.cols <- 20
mycolors <- colorRampPalette(brewer.pal(8, "RdBu"))(nb.cols)
mat=as.matrix(allsummarydt4)
# my_group <- c("1","1","2","3","3","3","3","4","4","5","5","5","5","5","5","5")
# ha=HeatmapAnnotation(categories=my_group,col = list(categories = c("1" = "red", "4"="orange","2" = "green", "3" = "blue","5"="pink")))
# Heatmap(mat, name = "rank", col = mycolors,rect_gp = gpar(col = "white", lwd = 2),row_dend_reorder = FALSE,cluster_columns = FALSE,top_annotation = ha)
#my_group <- c("1","1","2","3","3","3","3","4","4","5","5","5","5","5","5","5")
my_group <- c("1","1","3","3","3","3","4","4","2","5","5","5","5","5","5","5")
ha=HeatmapAnnotation(categories=my_group,col = list(categories = c("1" = "red", "4"="orange","2" = "seagreen", "3" = "blue","5"="pink")))
Heatmap(mat, name = "rank", col = mycolors,rect_gp = gpar(col = "white", lwd = 2),cluster_columns = FALSE,top_annotation = ha,row_order = c("cox1","cox2","cox3","cox4","pcox1","pcox2","pcox3","coxboost","gacox","gacoxboost","limmacoxboost","mtlr1","gamtlr","limmamtlr","rsf1","rsf2","survivalsvm","deephit","deepsurv","dnnsurv1"),column_order = c("Mean_hc","Mean_unoc","Mean_auc1","Mean_auc5","Mean_auc10","Mean_auc15","Mean_bs","Mean_time","Mean_memory","SD_hc","SD_unoc","SD_bs","SD_auc1","SD_auc5","SD_auc10","SD_auc15"))# nb.cols <- 18
# mycolors <- colorRampPalette(brewer.pal(8, "Blues"))(nb.cols)
# Heatmap(matrix(rnorm(6*18,0,1), ncol =18 ),col=mycolors)
#
# nb.cols <- 18
# mycolors <- colorRampPalette(brewer.pal(8, "Set3"))(nb.cols)
# Heatmap(matrix(rnorm(6*18,0,1), ncol =18 ),col=mycolors)
#
# nb.cols <- 18
# mycolors <- colorRampPalette(brewer.pal(8, "Set2"))(nb.cols)
# Heatmap(matrix(rnorm(6*18,0,1), ncol =18 ),col=mycolors)datafull3=datafull
datafull3$datasets=mapvalues(datafull3$datasets, from = seq(1,16,1), to = c("anz","us","veteran","lung","pbc","melanoma_clinical","melanomaitraq","melanomanano","gse1","gse2","ngene1","ngene2","mgene3","ngene4","ngene5","ngene6"))
clinical_part=datafull3[datafull3$datasets %in% c("anz","us","veteran","lung","pbc","melanoma_clinical"),]
omics_part=datafull3[datafull3$datasets %in% c("melanomaitraq","melanomanano","gse1","gse2","ngene1","ngene2","mgene3","ngene4","ngene5","ngene6"),]
c_part=clinical_part%>% group_by(model,variable) %>%dplyr::summarize( SD_value=sd(value,na.rm = TRUE))
o_part=omics_part%>% group_by(model,variable) %>%dplyr::summarize( SD_value=sd(value,na.rm = TRUE))
c_part1=tidyr::spread(c_part, variable, SD_value)
o_part1=tidyr::spread(o_part, variable, SD_value)
c_part1=as.data.frame(c_part1[,c(1,2,26)])
c_part2=as.matrix(c_part1[,-1])
rownames(c_part2)=c_part1$model
nb.cols <- 18
mycolors <- colorRampPalette(brewer.pal(8, "RdBu"))(nb.cols)
mat=c_part2
Heatmap(t(mat), name = "SD", col=colorRamp2(c(0.05, 0.3), c("white", "red")),cluster_rows = FALSE,cluster_columns=FALSE,rect_gp = gpar(col = "white", lwd = 2),width = ncol(mat)*unit(5, "cm"),
height = nrow(mat)*unit(0.1, "cm"))o_part1=as.data.frame(o_part1[,c(1,2,26)])
o_part2=as.matrix(o_part1[,-1])
rownames(o_part2)=o_part1$model
mat=o_part2
Heatmap(t(mat), name = "SD", col = colorRamp2(c(0.05, 0.3), c( "white", "red")),cluster_rows = FALSE,cluster_columns=FALSE,rect_gp = gpar(col = "white", lwd = 2),width = ncol(mat)*unit(5, "cm"),
height = nrow(mat)*unit(0.1, "cm"))